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Seagrass beds play a vital ecological role by regulating climate, maintaining biodiversity, and sequestering carbon. Achieving accurate classification of seagrass beds across both time and space is of significant importance for efficient monitoring. This paper addresses the challenges of seagrass bed classification in remote sensing imagery and the difficulty of achieving temporal and spatial transferability of classification model. Leveraging a long-term Landsat satellite image time series spanning from 1976 to 2022 in the Yellow River Delta, we enhance the DeepLab V3+ network and propose a cross-temporal-spatial domain seagrass bed transfer learning classification approach. We utilize selectively chosen images from a few years in partial regions for training, introduce freeze-training and-unfreeze-training during the model training phase, and then transfer the model to achieve cross-temporal and cross-temporal-spatial domain classification on images from other years and regions. Additionally, we discuss the factors influencing the classification results. The research results indicate that: (1)The method we proposed has shown good results, achieving classification accuracy of 83.17% and 80.99% for seagrass beds in both cross-temporal and cross-temporal-spatial domains. (2) The classification results indicate that this method achieves an average classification accuracy higher by 16.18% compared to networks such as UNet, PSPNet, HRNet, UNet++, SegFormer, etc. under cross-temporal conditions and higher by 17.56% under cross-spatiotemporal conditions for seagrass bed classification. (3) Based on the classification results, it is observed that from 1973 to 1986, the overall area of seagrass beds in the Yellow River Delta had an initial growth trend, with the largest seagrass bed area recorded in 1986, reaching 4281.289 hectares. Subsequently, the seagrass beds showed an overall decreasing trend, and by 2019 they had almost completely disappeared. The cross-temporal-spatial domain seagrass bed transfer learning classification method proposed in this paper can provide valuable support for the monitoring, restoration, management, and utilization of seagrass ecosystems. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of training deep learning models for fire smoke scene detection from multi-sensor, multi-spectral satellite imagery, where spectral bands vary and training data is scarce, especially for new sensors. We introduce a novel cross-sensor transfer learning approach enhanced by spectral pattern extraction, notably aided by a deep learning (DL) module called Input Amplification (IA). Our contribution includes the creation of two datasets, Landsat_smk2770 and Sentinel2_smk, with varying spectral bands. We employ a model named IA_VIB_SD which incorporates IA and is designed specifically for fire smoke scene detection. We investigated the transferability of the Landsat_smk2770-trained IA_VIB_SD model to the Sentinel2_smk dataset using various transfer learning techniques, and compared the performance of the transferred model with the benchmark IA_VIB_SD model exclusively trained on pure Sentinel2_smk data. Our proposed transfer learning approach resulted in a transferred model with an average accuracy 5% higher than the benchmark model. Notably, Our proposed transfer learning approach outperformed existing transfer learning methods by more than 1% in terms of the accuracy, even when trained on a mere 10% of the Sentinel2_smk dataset. This research advances fire smoke scene detection from multi-sensor, multi-spectral satellite imagery. [ABSTRACT FROM AUTHOR]

Automatic building detection from remote sensing images holds significant applications in various domains, including cartography, land-use change detection, urban planning, and 3D intelligent city construction. Driven by the innovation of deep learning technology, convolutional neural networks (CNNs) have emerged as a powerful framework for automatic interpretation, exhibiting hierarchical feature generation and robust representation for building extraction. However, the inherent computational limitations of convolutional operators hinder the acquisition of an effective global receptive field. Recent advancements in deep learning have witnessed the adoption of the multi-head self-attention (MHSA) mechanism by Transformer, presenting the superiority of global context construction. Nonetheless, this network structure encounters difficulties in effectively modelling two-dimensional spatial structure features with scale variation in semantic segmentation and often has huge parameters with high algorithm complexity. To address the above problems, this paper proposed a hybrid deep neural network synergistically harnessing the superiority of CNN and Transformer to extract buildings from remote sensing images. Meanwhile, an improved MHSA mechanism is developed to facilitate the construction of multiscale global representations, concurrently reducing computational complexity. Furthermore, the cross-level MHSA modules the semantic correlation between the deep and shallow-level features to refine spatial fine-grained information. The proposed method was comprehensively evaluated on two building datasets via quantitative analysis and visualization. The experimental results confirmed that the developed network and modules outperform other state-of-the-art methods and can effectively improve the accuracy (93.4% F1-score on the WHU dataset, 89.38% F1-score on the CAMB dataset) with high efficiency for building extraction. [ABSTRACT FROM AUTHOR]

Semantic segmentation of high-resolution remote sensing images is important in land cover classification, road extraction, building extraction, water extraction, etc. However, high-resolution remote-sensing images have a lot of details. Due to the fixed receptive field of convolution blocks, it is impossible to model the correlation of global features. In addition, complex fusion methods cannot integrate spatial and global context information. In order to solve these problems, this paper proposes a cross-linear attention network (CLANet) to capture spatial and context information in images. The structure consists of a spatial branch and a context branch. The spatial branch is constructed by stacked convolution to better capture spatial information. The context branch models the global information based on the transformer deformation module. In addition, to effectively fuse spatial and context information, this paper also designs a feature fusion module (FFM), which uses a cross-linear attention mechanism for feature aggregation. Finally, this paper conducts many experiments on the ISPRS Vaihingen and the ISPRS Potsdam datasets. Among them, 82.28% of mIoU achieves on the ISPRS Vaihingen dataset. The experimental results show that CLANet has better performance and effect than the methods in recent years. [ABSTRACT FROM AUTHOR]

Eddies in the Marginal Ice Zones (MIZ) contribute to an enhanced melting of sea ice by forcing contact between the sea ice and the warmer water off the ice edge. The horizontal current field can be retrieved by tracking the movement of ice floes between sequential satellite images. However, the traditional method of current retrieval imposes stringent requirements on the time intervals between sequential images, and the performance of the traditional method diminishes rapidly with increasing time intervals. In this paper, an improved method of retrieving currents from multi-sensor remote sensing images is proposed, which can significantly improve the accuracy of current retrieval. The method first extracts feature from remote sensing images based on texture features and a support vector machine model and then extracts prior information about the ice-edge eddy from the first two remote sensing images. Under the guidance of prior information, horizontal currents of the eddy are retrieved by tracking the movement of sea ice from sequential remote sensing images. The proposed method was used to retrieve currents from Sentinel-1 and Sentinel-2 sequential images over the MIZ of the Fram Strait. Comparisons with the traditional method show encouraging results that the proposed method can significantly reduce the number of mismatches and improve the reliability of current vectors. [ABSTRACT FROM AUTHOR]

Presence of cloud-covered pixels is inevitable in optical remote-sensing images. Therefore, the reconstruction of the cloud-covered details is important to improve the usage of these images for subsequent image analysis tasks. Aiming to tackle the issue of high computational resource requirements that hinder the application at scale, this paper proposes a Feature Enhancement Network(FENet) for removing clouds in satellite images by fusing Synthetic Aperture Radar (SAR) and optical images. The proposed network consists of designed Feature Aggregation Residual Block (FAResblock) and Feature Enhancement Block (FEBlock). FENet is evaluated on the publicly available SEN12MS-CR dataset and it achieves promising results compared to the benchmark and the state-of-the-art methods in terms of both visual quality and quantitative evaluation metrics. It proved that the proposed feature enhancement network is an effective solution for satellite image cloud removal using less computational and time consumption. The proposed network has the potential for practical applications in the field of remote sensing due to its effectiveness and efficiency. The developed code and trained model will be available at . [ABSTRACT FROM AUTHOR]

Atmospheric correction eliminates corruption in reflectance captured by satellite images due to atmospheric elements like gases, aerosols, and water vapours. Existing physics-based approaches employ radiative transfer models constructed using lookup tables computed for different atmospheric parameters. However, these approaches are computationally expensive and rely on estimates of parameters that are difficult to sense accurately. This paper proposes a deep learning model as an alternative to physics-based approaches. We present an end-to-end deep neural network trained on seasonally and spatially rich Landsat 8 satellite images without explicit atmospheric parameterization along with our analysis and its validation. We validate the model's effectiveness vis-a-vis Landsat 8's Land Surface Reflectance Code – LaSRC results in $RMSE \sim 0.042$ R M S E ∼ 0.042 , $SSIM \sim 0.97$ S S I M ∼ 0.97 , and correlation coefficient $r \sim 0.99$ r ∼ 0.99. For ground measurements by RadCalNet, the proposed model has an $RMSE \sim 0.053$ R M S E ∼ 0.053 , $SSIM \sim 0.90$ S S I M ∼ 0.90 , and $r \sim 0.88$ r ∼ 0.88. The results show that the model accurately predicts surface reflectance and correlates highly with reference data. [ABSTRACT FROM AUTHOR]

Domain adaptation is the ability to improve the learning efficiency of the target domain by using the prior knowledge of the source domain. When applied to new tasks in the target domain, the performance of domain adaptation trained in the source domain declines sharply. The purpose is to realize a new retrieval task in the target domain using only a small number of labelled data samples, with the aid of the prior knowledge learned in the source domain. This paper focuses on semi-supervised domain adaptation in remote sensing image retrieval. The contributions of this paper are threefold. First, we construct Gabor-based CNNs to facilitate the networks to effectively capture the texture information of images. Second, we propose a cross-domain knowledge transfer strategy based on dual Gabor neural network learning. Third, we propose an unsupervised random feature mapping method based on probability distance. A large number of experiments have been conducted on UCM, WHU-RS, RSSCN7, AID, and PatternNet datasets. The results show that this method greatly improves the retrieval accuracy on the target domain and obtains state-of-the-art retrieval accuracy. The source code is available at . [ABSTRACT FROM AUTHOR]

Microbial biofilms have received great attention in the last few decades from both aquatic ecologists and biogeomorphologists. Most recently, this has focused on mapping biofilms to understand their spatial distributions and ecosystem services. Such studies often involve the use of satellite imagery, which typically provides large temporal and spatial scales and wide-range spectral information. Although satellites have the advantage of multi- and hyper-spectral sensors, images often have low spatial resolution that limits their use in river studies, where both rivers are narrower and stream processes occur at resolutions smaller than the footprint of satellite sensors. Spatial resolution is sensor quality dependent but also controlled by sensor elevation above the ground. Hence, high resolutions can be achieved either by using a very expensive sensor or by decreasing the distance between the target area and the sensor itself. To date, sensor technology has advanced to a point where multi- or even hyper-spectral cameras can be easily carried out by an Uncrewed Aerial Vehicle (UAV) at unprecedented spatial resolutions. Where such sensors have high spectral resolution, they are often prohibitively expensive, especially as their use in extreme environments such as glacial forefields risks UAV damage. In this paper, we test the performance of visible band ratios in mapping biofilms in an Alpine glacier forefield characterized by a well-developed and heterogeneous stream ecosystem but using a low-cost UAV. The paper shows that low-cost and consumer grade UAVs can be easily deployed in such extreme environments, delivering both quality RGB images for photogrammetric (SfM-MVS) processing and sufficient spectral information for benthic biofilm mapping at high temporal and spatial resolution. RGB cameras are an alternative to expensive multi- or hyper-spectral cameras. Phototrophic biofilms can be detected and mapped through visible band ratios. High-temporal and high-resolution imagery can be collected by consumer-grade UAVs. Biofilm presence is restricted to stable and water-fed terraces in summer. [ABSTRACT FROM AUTHOR]

As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects. [ABSTRACT FROM AUTHOR]

Image semantic segmentation methods based on convolutional neural networks rely on supervised learning with labels, and their performance often drops significantly when applied to unlabelled datasets from different sources. The domain adaptation methods can reduce the inconsistency of feature distribution between the unlabelled target domain data used for testing and the labelled source domain data used for training, thus improve the segmentation performance and have more practical applications. However, in the field of remote sensing image processing, if the spatial resolutions of the source domain and the target domain are different and this problem is not to be solved, the performance of the transferred model will be affected. In this paper, we propose a bidirectional semantic segmentation method based on super-resolution and domain adaption (BSSM-SRDA), which is suitable for the transfer learning task of a semantic segmentation model from a low-resolution source domain data to a high-resolution target domain data. BSSM-SRDA mainly consists of three parts: a shared feature extraction network; a super-resolution image translation module, which incorporates a super-resolution approach to reduce spatial resolution differences and visual style differences of the two domains; a domain-adaptive semantic segmentation module, which combines an adversarial domain adaptation approach to reduce differences at the output level. At the same time, we design a new bidirectional self-supervised learning algorithm for BSSM-SRDA that facilitates mutually beneficial learning of the super-resolution image translation module and the domain-adaptive semantic segmentation module. The experiments demonstrate the superiority of the proposed method over other state-of-the-art methods on two remote-sensing image datasets, with mIoU improvements of 2.5% and 3.2%, respectively. Code: [ABSTRACT FROM AUTHOR]

Remote sensing has become an important technique for monitoring vegetation phenology. The quality of remote-sensing images and derived products is key to successful extraction of vegetation phenophases. There is a need to develop quality management methods to evaluate the data uncertainty and assist the removal of the noises. This paper developed a shape quality assurance score threshold (SQAT) method which accounts for the trend in satellite-derived vegetation index associated with the process of vegetation growth. The proposed method was tested on six widely used methods for extracting vegetation phenophases. Results showed that the SQAT method can effectively identify noises in the vegetation index time series and improve the accuracies of estimated start of season (SOS) and end of season (EOS) of the six methods. After removal of identified noises, the Pearson correlation coefficient (r) averagely increased by 8% for SOS, and 11% for EOS. Regression analyses of vegetation phenophases between the PhenoCam observations and MCD12Q2 product showed that the proposed method performs better than the QA score of MCD12Q2 for quality management. This paper provides promising method for quality management; it has the potential to reduce the uncertainty of the vegetation index time series that can support studies of vegetation phenology monitoring. [ABSTRACT FROM AUTHOR]

Multi-temporal remote sensing imagery has the potential to classify river landforms to reconstruct the evolutionary trajectory of river morphologies. Whilst open-access archives of high spatial resolution imagery are increasingly available from satellite sensors, such as Sentinel-2, there remains a fundamental challenge of maximising the utility of information in each band whilst maintaining a sufficiently fine resolution to identify landforms. Although image fusion and downscaling methods on Sentinel-2 imagery have been investigated for many years, there is a need to assess their performance for multi-temporal object-based river landform classification. This investigation first compared three downscaling methods: area to point regression kriging (ATPRK), super-resolution based on Sen2Res, and nearest neighbour resampling. We assessed performance of the three downscaling methods by accuracy, precision, recall and F1-score. ATPRK was the optimal downscaling approach, achieving an overall accuracy of 0.861. We successively engaged a set of experiments to determine an optimal training model, exploring single and multi-date scenarios. We find that not only does remote sensing imagery with better quality improve river landform classification performance, but multi-date datasets for establishing machine learning models should be considered for contributing higher classification accuracy. This paper presents a workflow for automated river landform recognition that could be applied to other tropical rivers with similar hydro-geomorphological characteristics. Choice of downscaling approach influences the performance of river landform classification from satellite imagery and should be considered in river and flood management. An efficient and straightforward operating workflow was developed for automated river landform classification with high accuracy that supports an improved understanding of the use of machine learning approaches in river landform recognition. Freely available and easy-to-access remote sensing datasets can help extend the operating workflow to difficult-to-access or remote regions and allow for complete regional and/or national coverage. [ABSTRACT FROM AUTHOR]

Classification-based methods for estimating water quality parameter (WQP) using remote sensing have shown great application potential in inland waters. Water classification algorithms have seen progress in water remote sensing. In this paper, we conducted the Secchi Depth value (ZSD) estimation based on a global water typology for the Wuhan area. Firstly, we classified the water into seven types using the Sentinel-2 data. The procedure was based on spectral angle mapping (SAM) of the 13 inland water optical types (IWOTs). Afterwards, seven IWOTs were summarized into four categories for Wuhan area. We then developed empirical models for each water category by stepwise multiple linear regression, generalized regression neural network (GRNN), and sparse spectrum Gaussian process regression (SSGPR), and applied the better approaches (GRNN and SSGPR) to three full satellite images (27 October 2018, 10 May 2019, and 29 July 2019). Finally, the retrieved results were validated using in situ-satellite match-ups and compared with the results based on unclassified imagery. With root-mean-square error (RMSE) of three satellite-derived results reduced from 0.32 m (without classification) to 0.16 m (with classification), and mean absolute percentage error (MAPE) reduced from 52% to 18%, from 0.51 m (MAPE = 57%) to 0.19 m (MAPE = 21%), and from 0.18 m (MAPE = 35%) to 0.09 m (MAPE = 17%), ZSD estimations over optically complex waters were improved based on this water classification. Due to its low cost and ease of operation, the SAM – derived classification applied in this paper provides a possibility for dynamic and high-precision monitoring for water management. [ABSTRACT FROM AUTHOR]

Ship detection for SAR imagery plays a crucial rule in the field of remote-sensing image processing. Superpixel methods have attracted enormous interest in the recent years, resulting in superpixel-based CFAR (Constant False Alarm Rate) ship detection methods for SAR images become a hot research issue. However, existing methods take superpixel generation as a preprocessing, and CFAR detection is essentially carried out at the pixel level, and the influence of speckle noise has not been fundamentally overcome. This paper innovatively proposed a real superpixel-level CFAR detection method, which takes superpixels instead of pixels as the minimum processing primitives on the basis of reliable superpixel segmentation. Firstly, in order to obtain the background superpixel region of each under test superpixel more accurately and efficiently, we designed a ring topology, which was specially developed for non-Euclidean structure data like superpixels. Secondly, the Johnsonsu function is used to fit the superpixel clutters from the background ring, and the detection threshold was obtained by a given false alarm rate. Finally, the candidate targets were further optimized to carry out accurate detection results. Experimental results based on multiple sets of real SAR images shown that our method has significant advantages in terms of detection rate and false alarm rate compared to the traditional K-CFAR and the existing superpixel-based CFAR methods. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing scene classification is a widely applicable task. Due to the diversity of natural scenes and acquisition methods, in different satellite images, scenes of the same class are often variable in texture, background, illumination and spatial resolution. Thus, it is hard for a remote sensing sense database to contain enough representative images. In this case, the generalization error of traditional supervised classification methods might be too large to generate ideal results. Domain adaptation (DA) has been applied to image classification by reducing feature distribution discrepancy between the source domain (where labels are available) and the target domain (where images need to be classified). In this paper, we propose an unsupervised adversarial domain adaptation method boosted by a domain confusion network (ADA-BDC) which aims at adapting the images from different domains to appear as if drawn from the same domain and improve the transferability of our classifier. For this purpose, the feature extractor in ADA-BDC makes the source and target distributions closer by training a Generative Adversarial Nets (GAN) model. After that, a transferred classifier trained by transferred source domain features is able to acquire a better classification accuracy on the target domain than a non-transferred classifier. In this paper, the experiments are conducted on four remote sensing scene benchmark datasets which are different in spatial scale, resolution, land-cover pattern, etc. Experimental results show that our proposed method is able to improve the transferability across different datasets and improve the classification overall accuracy by 17.18% on average. The comparative experiments demonstrate that the proposed DA network outperforms the compared state-of-the-art domain adaptation methods on remote sensing image scene classification. [ABSTRACT FROM AUTHOR]

Object detection is a key technique for automatic information extraction, analysis and understanding of high spatial resolution remote-sensing images (HSRIs). Object anchor scales are a critical factor for object detection of HSRIs based on convolutional neural networks (CNNs). With respect to adaptively obtaining optimal object anchor scales for object detection of HSRIs, this paper proposes a novel object detection method for HSRIs based on CNNs with optimal object anchor scales. First, optimal object anchor scales for object detection of HSRIs are obtained using an adaptive object-scale learning operator. Then, a CNN object detection framework for HSRIs is designed based on optimal object anchor scales. Using multiple-object detection datasets, the proposed method is compared with some state-of-the-art object detection algorithms. Experimental results show that the proposed method can achieve 90.42%, 91.98% and 85.07% mAP on WHU-RSONE, UCAS-AOD and HSRC2016, respectively, and outperforms state-of-the-art object detection algorithms. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNN) have been widely applied in various fields, especially in the field of object detection. Deep CNN-based models showed great advantages over many traditional methods, even so, there are still many specific problems in the application of certain scenarios. In very high resolution (VHR) remote-sensing image datasets, the uncertainty of the object direction angle causes big trouble to the learning of the detector. Although the pooling operation can slightly alleviate the deviation caused by small angle, the feature learning of the objects with larger angle rotation still relies mainly on the sufficiency of sample data or effective data augmentation, which means the insufficiency of the training instances may cause serious performance degradation of the detector. In this paper, we propose a multi-angle box-based rotation insensitive object detection structure (MRI-CNN), which is an extended exploration for typical region-based CNN methods. On the one hand, we defined a set of directionally rotated bounding boxes before learning, and restricted the classification scene in a small angular range by rotated RoI (Region of Interest) pooling. On the other hand, we proposed a more effective screening method of bounding boxes, enabling the detector to adapt to diverse ground truth annotation methods and learn more accurate object localization. We trained our detector with different datasets containing different amount of training data, and the test results showed that the method proposed in this paper performs better than some mainstream detection methods when limited training data are provided in VHR remote-sensing datasets. [ABSTRACT FROM AUTHOR]

Morphological processing has found several applications in image analysis and pattern recognition. Some of these techniques, known as morphological reconstruction algorithms, have been employed for land cover classification in remote sensing data. In this paper, we analyse the mathematical foundations, applications, and limitations of reconstruction by dilation and by erosion oriented to urban extraction, using Sentinel-2 satellite data. Different techniques oriented to the proper determination of the marker and mask images, the basis for reconstruction, are proposed in this manuscript. In addition, in order to diminish the long computation time required for reconstruction, two parallel implementations using Multi-core and GPU, are proposed. According to our research, these algorithms can be considered as effective and non-supervised solutions for urban extraction applications based on multispectral satellite imagery. [ABSTRACT FROM AUTHOR]

The multiclass change detection task aims to segment an area of land change and to identify the type of ground change exploiting bi-temporal remote-sensing images. In recent years, this technology has become an important means of analysing remote-sensing data and has shown great potential in accurately understanding changes on the earth's surface. However, the existing methods are mainly for binary change detection tasks and cannot detect the specific information of the change category. Hence, in this paper, we propose an end-to-end densely connected network, entitled Y-Net, employing a Siamese structure for multiclass change detection. The proposed network uses a dual-stream DenseNet to extract bi-temporal change features during the encoding stage and introduces the attention fusion mechanism for the decoding stage to enhance the attention to changing features. Add the dice loss to reduce the impact of sample imbalance on multiclass change detection. The suggested network utilizes a deep supervision strategy for each decoding layer to achieve change graph reconstruction of different levels. Y-Net is evaluated on the challenging multiclass change detection data set SECOND, and the ablation study verified the effectiveness of the attention fusion module and dice loss for multiclass change detection, considering both visual interpretation and quantitative evaluation, confirming that the proposed method is superior to current state-of-the-art change detection methods. [ABSTRACT FROM AUTHOR]

The rational function model (RFM) is widely used for high-resolution satellite imagery allowing its use in GIS and remote sensing applications. RFM can be seen as a non-parametric model that establishes a geometric transformation between image and ground coordinate spaces. Unlike the rigorous models, it constitutes an easy and simple approach where any prior knowledge about the sensor's physical configuration and parameters is required. For a precise ortho-rectification, RFM requires a large number of accurate and well-distributed ground control points (GCPs) which is a time-consuming and expensive process. Moreover, adjustment errors may occur due to the over-parameterization resulting from the high number of coefficients in this model. In fact, coefficients in RFM do not have any physical meaning, which makes it impossible to find their best combination. In order to overcome this problem, optimization based on evolutionary algorithms seems to be an appropriate solution. In this paper, a hybrid binary of particle swarm optimization (PSO) method is proposed. It combines the PSO concept with that of genetic algorithms by adding two new operations in the binary version of PSO consisting of crossover and mutation in order to increase the convergence speed and avoid the local optimum phenomenon. The proposed algorithm has been applied on two data sets provided by the Alsat-2 Algerian satellite in different configurations, namely standard scenes and strips which are a group of scenes acquired continuously from the same orbit. To the best of our knowledge, this is the first work dealing with the use of PSO for the RFM optimization aiming at the geometrical modelling of a strip of images. The obtained results have shown that the proposed HPSO-RFM method outperforms other competing methods by achieving, for the best scenario, an accuracy improvement of about 38% and 21% over PSORFO and PSO-KFCV, respectively. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Over the last two decades, there has been a significant improvement in the quality and volume of the remote-sensing images. With the extensive availability of the images, these are used in many applications such as change detection, image mosaicing, and image fusion where image registration is very necessary. However, image registration is a challenging task in remote sensing due to the following reasons: geometric differences between images, intensity differences, and noise affect. Over the years, wide variety of algorithms have come into existence to handle these issues. In this paper, a comprehensive review is presented on these remote sensing image registration methods. At first, the general information of remote sensing image registration, its classifications, and application areas are provided. Then, an exhaustive review is given on the existing methods and their merits as well as shortcomings are mentioned. Finally, a comparative analysis is provided for the state-of-the-art methods and current challenges of this research are presented. [ABSTRACT FROM AUTHOR]

Ｗith the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information this paper, a tiny vehicle detection method based on spatio-temporal information is proposed to constrain the significance of the image. Firstly, the background modelling method is used to obtain the motion heat map of the image and constrain the motion region. A significance detection method for small targets was used to obtain the significance mapping of these regions. Finally, the detection results were optimized by combining the significance neighbourhood information and the time information between frames to output the binary target detection map. Finally, taking different urban road scenes in 'Jilin-1'satellite video as examples and compares a variety of existing algorithms. Experiments prove that the proposed algorithm can maintain false alarm rate of less than 10% when the detection accuracy and recall rate reach 85% and has certain anti-interference ability in the image environment with satellite Angle deviation. [ABSTRACT FROM AUTHOR]

Detecting geospatial objects, especially small, time-sensitive targets such as airplanes and ships in cluttered scenes, is a substantial challenge in large-scale, high-resolution optical satellite images. Directly detecting targets in countless image blocks results in higher false alarms and is also inefficient. In this paper, we introduce a hierarchical architecture to quickly locate related areas and detect these targets effectively. In the coarse layer, we use an improved saliency detection model that utilizes geospatial priors and multi-level saliency features to probe suspected regions in broad and complicated remote sensing images. Then, in the fine layer of each region, an efficacious end-to-end neural network that predicts the categories and locations of the objects is adopted. To improve the detection performance, an enhanced network, adaptive multi-scale anchors, and an improved loss function are designed to overcome the great diversity and complexity of backgrounds and targets. The experimental results obtained for both a public dataset and our collected images validated the effectiveness of our proposed method. In particular, for large-scale images (more than 500 km2), the adopted method far surpasses most unsupervised saliency models in terms of the performance in region saliency detection and can quickly detect targets within 1 minute, with 95.0% recall and 93.2% precision rates on average. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) techniques including Convolutional neural network (CNN), Recurrent neural network (RNN), and Recurrent-Convolutional neural network (R-CNN) have been extensively used to classify the remotely sensed data. Out of various deep learning algorithms, CNN-based algorithms are most widely used for the satellite image classification. Despite the improved performance of CNN, it also requires various hyper-parameters for training the network architecture to achieve the desired classification accuracy. Keeping in view the fact that the accuracy achieved by any classification algorithms is influenced by a suitable choice and value of hyper-parameter, this paper discusses the influence of several hyper-parameters on the classification accuracy of CNN classifier using three remote sensing datasets. The aim of this study is not to propose a set of values of different hyper-parameters but to study their influence on land cover classification accuracy with remote sensing datasets. Experimental results from the study indicate that various hyper-parameters affect the performance of CNN classifier to different extent suggesting a need to select the optimal value of these hyper-parameters for land cover classification studies using considered datasets. [ABSTRACT FROM AUTHOR]

Due to the different characteristics of satellite imagery, pan-sharpening has become an important field of remote sensing science. It is a reliable method to fuse the high-resolution panchromatic (Pan) image with the low-resolution multispectral (MS) images to generate a composite high-resolution MS (pan-sharpened) image. In this paper, we propose a robust pan-sharpening technique which combines the nonsubsampled contourlet transform (NSCT) with kernel principal component analysis (KPCA). An enhancement method is executed on the source MS image to retain the maximum spatial information present in the MS bands based on the intensity hue saturation (IHS) colour space transform. The proposed pan-sharpening technique is performed as follows: first, the source Pan and MS images are divided into high and low-pass coefficients by NSCT. Second, KPCA is used to propose an effective fusion rule for choosing appropriate low-pass NSCT coefficients for fusion. The high-pass coefficients are fused by proposing an enhanced sum modified Laplacian method (SML). Finally, the final pan-sharpened image is obtained by performing an inverse NSCT and inverse IHS transform on the fused low and high-pass coefficients. Four different groups of satellite image datasets are utilized in the experiments, which show that the proposed technique can both preserve the spatial details of the source images well and avoid spectral distortion. In addition, different image fusion quality metrics are adopted to evaluate the spectral and spatial qualities of the pan-sharpened image. Compared to the state-of-the-art pan-sharpening methods, the proposed technique achieved better performance in balancing spectral and spatial information and improved pan-sharpening results in terms of both visual quality and objective measurements. [ABSTRACT FROM AUTHOR]

Satellite image classification is the core of satellite image interpretation. The traditional shallow learning classification algorithm is difficult to extract remote sensing image features effectively, resulting in low classification accuracy. The deep neural network can automatically realize the feature analysis. However, due to the similarity of the optical properties of the cloud and snow, the existing deep learning model is difficult to obtain satisfactory results for the cloud and snow classification in the plateau. Meanwhile, too many parameters of deep learning lead to slow detection speed, which is difficult to meet the real-time requirements of detection. Furthermore, the deep learning method is prone to model degradation. In view of the existing problems, this paper proposes a dilated multi-scale cascade forest method to realize the classification of satellite cloud images. Multi-scale scanning increases the diversity of feature vector extraction and improves the separability of features. The dilated structure is designed to expand the feature receptive field, it improves the efficiency of feature extraction without losing the texture and spatial correlation information of the original image. In addition, the number of layers of network cascading does not need to be customized, it can be determined automatically according to the performance of the system. The proposed method effectively improves the accuracy of cloud/snow classification. Experiments on the imagery of HuanJing satellites A and B (HJ-1A/1B) in China demonstrate that the proposed model can get better classification results than existing studies. The time complexity of the proposed model is lower than existing works, hence it is practical in real-time and accurate remote sensing image classification. [ABSTRACT FROM AUTHOR]

Updating the physicochemical properties of surface water is important to water quality researchers and decision-makers. Routine surface water quality monitoring methods are inadequate; whereas, remote sensing (RS) is a source of processing data in both spatial and temporal domains. However, RS is a good tool of monitoring freshwater ecosystems, accurate modelling of surface water quality parameters from satellite imagery is still a challenging task. High correlation between the adjacent satellite signals, which creates redundancy, is one of the main challenges that negatively affect the stability of the developed models. Therefore, this paper focuses on the development of a novel approach for modelling concentrations of both turbidity (T) and total suspended solids (TSS) using Landsat-8 tasselled cap transformation (TCT). As a result, robust models were developed between measurements from satellite-derived T and TSS and in situ measurements with coefficient of determination values of 0.854 and 0.812, respectively. These findings confirmed the possibility of using TCT to establish strong models for assessing surface water quality due to its capability of transforming a set of correlated data onto a convenient set of uncorrelated data, which maintain the major information associated with a physical phenomenon. [ABSTRACT FROM AUTHOR]

Change detection has been a research hotspot in remote sensing fields for decades. However, the increasing use of very high-resolution (VHR) remote sensing images have introduced more difficulties in change detection because of the complex details these images contain. In this paper, we propose a novel deep learning architecture for change detection composed of a Trisiamese subnetwork and a long short-term memory (LSTM) subnetwork that fully utilizes the spatial, spectral and multiphase information and improves the change detection capabilities for VHR remote sensing images. Multiscale simple linear iterative clustering (SLIC)-based image segmentation is first performed on multitemporal images at different image scales to obtain edge information-based objects. A Trisiamese subnetwork with six inputs can extract abundant spectral-spatial feature representations; the LSTM subnetwork then uses the extracted image features to effectively analyse the multiphase information in bitemporal images. The proposed method has the following advantages: 1) it can fully utilize the significant spatial information to improve the detection task; 2) it combines the advantages of convolutional architectures for image feature representation and recurrent neural network (RNN) architectures for sequential data representation, unlike most of the algorithms that use either method or that merely use image differencing or stacking operations. The controlled experiments reveal that the multiphase information extracted by the LSTM subnetwork is important to improve the accuracy of the change detection results. The influence of the Trisiamese subnetwork on change detection is even more significant than that of the LSTM subnetwork. Comparisons with other state-of-the-art change detection methods indicate that in areas with clear surface features and limited interference, the proposed method obtains more competitive results compared to state-of-the-art methods, and in regions where the changed objects occur in complex patterns, the proposed method exhibited an ideal performance. [ABSTRACT FROM AUTHOR]

In remote sensing applications, it is important to have a dataset that has both high-spectral and high-spatial resolutions. Hyperspectral (HS) imaging achieves a very high spectral resolution, but its spatial resolution is significantly reduced due to very narrow spectral bands of the sensor. One alternative solution to increase its spatial resolution is pan-sharpening, similar to the pan-sharpening used for multispectral (MS) images. Although many pan-sharpening methods have been widely used to increase the spatial resolution of MS images, adapting such methods for HS pan-sharpening is still very challenging, because almost 90% of the HS bands do not fall within the spectral coverage of the Pan band. It has already been a challenge in MS pan-sharpening to fuse the bands that are beyond the Pan spectral coverage. It is even more challenging for HS pan-sharpening to fuse the bands that are beyond the Pan spectral range. This paper investigates the feasibility of using widely used pan-sharpening methods to fuse the HS bands that are beyond the Pan spectral range. An adaptive band selection method is proposed to help to identify the HS bands across the entire HS spectral range that can be pan-sharpened with high spectral fidelity. This is achieved based on statistical measurements and analyses between Pan and the HS bands. EO-1 satellite images are used for the experiment. Results show that using the adaptive band selection method, appropriate HS bands can be effectively selected across the entire spectral range to produce pan-sharpened HS bands with minimized spectral distortion. [ABSTRACT FROM AUTHOR]

Precise 3D registration of Light Detection and Ranging (LiDAR) data and High-Resolution Satellite Image (HRSI) is the prerequisite in the many remote sensing applications. An automatic registration process involves two main steps including the detection of corresponding entities and the estimation of a relating mathematical model. Typical matching techniques, which are generally developed to match consistent data sources, are prone to fail in case of matching between heterogeneous data sources (e.g. LiDAR and optical images). This paper proposes a three-step method to give in hand an accurate and automatic 3D registration technique between LiDAR data and the HRSI. The first step introduces a new product called Optical Consistent LiDAR Product (OCLP) which is meant to be consistent with HRSI from the radiometric point of view. The OCLP is generated using raster maps of the LiDAR heights and intensities along with information of the sun position at the acquisition time of HRSI. This new product proved to be very promising as a matching entity with HRSI. In the second step, a 3D model is estimated robustly through the matched points identified by the well-known Scale Invariant Feature Transform (SIFT) technique between the OCLP and the HRSI. The last step of the proposed method aims to strengthen this 3D model which is accomplished via iterative closest edge points matching technique. To do so, the coarse 3D model is iteratively improved based on the matching results obtained on the image edges. The proposed method was implemented on 20 different datasets containing various urban textures. The results indicate the effectiveness of the proposed method since in some cases it could achieve to sub-pixel accuracies which are the utmost expectation for a registration technique. [ABSTRACT FROM AUTHOR]

Mountains are an important kind of landform on the earth's surface. Due to harsh mountainous environment, such as steep slopes and cliffs, remote sensing has become an indispensable tool for surveying mountain areas instead of traditional ground surveys. However, the accuracy of current land cover products derived from remote sensing in mountain areas is still low. In this paper, we propose a three-level architecture for land cover classification in mountain areas. Topographic partitioning is first performed in order to partition a large area into several smaller zones, and then, multiresolution segmentation is implemented in each individual zone. Thus, we can obtain initial geo-semantic objects with terrain, spectrum and texture homogeneities. A fully convolutional network (FCN)-based classifier (U-Net) is further introduced for supervised classification of land cover. From the perspectives of both visual interpretation and quantitative evaluation, the proposed method achieved robust and high-precision results for all land cover types. We also investigate the contributions of multimodal features for classification accuracy improvement. First, the results showed that additional features resulted in higher classification accuracies than 3-features only; 6-features achieved the best performance on farmland, impervious surfaces and coniferous forests, while 5-features performed well on water and broad-leaved forests. The elevation feature did not have a positive effect on water and broad-leaved forests, which can be explained by their physical distribution in the landscape. Second, the most significant improvement was achieved on water (Kappa coefficient increased from 0.741 to 0.924), followed by coniferous forests (Kappa coefficient increased from 0.629 to 0.805), whereas only a minor improvement was observed for the other three types. Furthermore, the accuracies of farmland and impervious surfaces remained relatively high even without the assistance of additional features, and texture feature plays a key role. The final land cover map was generated by combining the optimal results of each type via a hierarchical integrating strategy. The overall accuracy of classification achieved 90.6%. [ABSTRACT FROM AUTHOR]

Most of the research in remote sensing is focused on developing a well-defined and reliable automated process for the extraction of information from different types of imagery. This paper deals with the extraction of linear anthropogenic objects (especially buildings) in hilly areas. The analysis of digital surface models from hilly terrain data is still challenging, especially for highly sloped landscapes. Commonly, the first task to study satellite data is to separate ground and object points as a preparatory step for further object classification. In this paper, object and ground separation in hilly terrain (PAN image of Indian Remote Sensing (IRS)-1C satellite) is addressed by employing wavelets. Then the extraction of desired objects (buildings) is done by employing the marker-controlled watershed segmentation. Experimental results demonstrate the method to be effective. [ABSTRACT FROM AUTHOR]

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50-100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state-of-the-art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared. [ABSTRACT FROM AUTHOR]

In clustering analysis of remote sensing imagery, a commonly held assumption is that the feature space can be modelled as a mixture of Gaussians. However, the assumption is not true for many real data and therefore incorrect classification results are often obtained by parametric methods. Nonparametric methods in feature space analysis can avoid the use of the normality assumption. Arbitrarily structured feature spaces can be analysed only by means of nonparametric methods as these methods do not have embedded assumptions. The mean shift is a basic computational module of the nonparametric technique in pattern recognition. The mean shift procedure can be used to cluster multispectral remote sensing imagery. Earlier clustering techniques based on the mean shift used a single scale over the entire feature space and were not feasible for the analysis of complex multimodal feature spaces. In this paper, we present an adaptive mean shift method in which local scale information is involved. The proposed algorithm can find arbitrary density, size and shape clusters in remote sensing imagery. The method is a simple technique of unsupervised image classification. We demonstrate its advantages in classification accuracy over earlier methods described in this paper. [ABSTRACT FROM AUTHOR]

Frequently cloudy conditions in moist tropical regions often cause difficulty in acquiring good-quality remote-sensing images. This paper explores approaches to detect clouds/hazes and their cast shadows on IKONOS images. Two approaches based on maximum and minimum filters and the second-moment texture measures were tested. The detected clouds/hazes and their cast shadows in one image were then replaced with the pixel values from the same location of another image after image normalization. This research indicates that both the approaches developed in this paper have successfully detected the clouds/hazes and their cast shadows on IKONOS images. In particular, the second-moment texture approach is recommended. [ABSTRACT FROM AUTHOR]

These two papers deal with a new method of data transformation. By analysing grey level curves (broken lines) of various ground features in image bands of different satellites, we have found that, inherent in 3- or 4-band satellite images (SPOT, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS) there are three basic remote-sensing characteristics as follows: (1) the general radiance level L; (2) the visible-infrared radiation balance B; and (3) the band radiance variation vector (direction and speed) V. However, inherent in 5- or 7-band satellite images (NOAA, TM), besides the above three, there is an extra basic remote-sensing characteristic, i.e. the thermal radiation intensity I. This is denoted by thermal bands, i.e. the TM band 6 or NOAA (AVHRR) bands 4 and 5, which are relatively independent and can be used directly, and hence are unnecessary for information extraction or data transformation. Therefore, the data transformation only lies in extracting the L, B and V from original satellite images. Furthermore, we have also found that there are three basic ground-cover elements on the Earth's surface: i.e. the bare land (in a broad sense), the vegetation and the water body, which, in different proportions, constitute all ground cover. Moreover, there are three basic (primitive) colours on colour image (including colour composite of satellite images): i.e. red, green and blue, which generate all colours on the colour image. Further research has revealed that the three basic remote-sensing characteristics, the three basic ground-cover elements and the three basic colours on the composite can conceptually constitute a three-to-three corresponding regular triangle scheme. Perhaps a good method of data transformation should make the scheme realistic, i.e. make the three 'threes' all correspond to each other. The research presented here has completed this task by regression calculations and selection of specific variables. First, the methodology and transformation equations for TM images are discussed. The transformed L, B and V images have relatively independent and equally distributed information as well as clear and definite physical, mathematical and geographical significance. They can be used effectively for generating high-quality colour composites, on which the red, green, blue, yellow, pink, cyan and other colours of various kinds are all generated and all pure, saturated, equilibrated, meaning-definite and close to the colours of ground features in nature. As a result, interpretations and discriminations of ground features can be easier and conducted not only by experience, but also by logic. The L, B and V images can also be used effectively for classification and digital analysis of ground features. With regard to the transformation equations for SPOT, NOAA, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS images and the method application will be dealt with in the second paper. [ABSTRACT FROM AUTHOR]

Compared to remote sensing images of medium or low spatial resolution, high-resolution remote sensing images can provide observation data containing more detailed information for georesearch. Accordingly, an important issue for current computer and geoscience experts is to develop useful methods or technology to extract information from these high-resolution satellite images. As part of a series of research into object extraction, this paper focuses mainly on the extraction of bridges over water from high-resolution panchromatic satellite images. Since bridges over water are obviously adjacent to water in remote sensing images, this paper proposes a practical knowledge-based bridge extraction method for remote sensing images of high spatial resolution. The steps involved are: water extraction based on Gauss Markov Random Field (GMRF)-Support Vector Machine (SVM) classification methods which use a SVM to classify the image based on textural features expressed by a GMRF; image thinning and removal of fragmented lines; main trunk detection by width; vectorization; and feature expression. Finally, tests are described for two pieces of panchromatic IKONOS satellite images with a 1 m resolution. The experimental results show that the proposed method is suitable for images with a single-peak histogram (contrast between water and land is sharp) or a multi-peak histogram (greyscale value of water is close to that of land). [ABSTRACT FROM AUTHOR]

Edge detection of an image by remote sensing is an important tool for the detection of oil spilled on sea. There are many algorithms for edge detection by image processing. Each algorithm has its own advantages and disadvantages for different images. In this paper, the author makes some improvements to the Pal–King fuzzy edge detection algorithm and proposes an algorithm combining improved fuzzy theory and a genetic algorithm for the detection of oil spilled on the sea by remote sensing. The Pal–King fuzzy detection algorithm has a good capability for the detection of fuzzy and thin edges. However the complex and large calculation and the fixed threshold value do not suit some kinds of image processing. The algorithm presented in this paper is composed of two parts: an improved fuzzy enhancement algorithm, which simplifies the complex G and G -1 calculation in the Pal–King algorithm; a genetic algorithm with which we are able to obtain the threshold value precisely and quickly for image processing. Finally the paper gives the results of image processing using the algorithm mentioned above and the Pal–King algorithm. Through comparison, we can conclude that the processing results obtained using the algorithm suggested in this paper are more legible than by the Pal–King algorithm. [ABSTRACT FROM AUTHOR]

Colour is widely used in remote sensing work. In many instances, the use of colour conveys additional information both visually and scientifically. Remote sensing satellites view the earth in different spectral bands, viz. near infrared (NIR), red, green, and blue bands, in a conventional multispectral imaging system. In the absence of a blue channel, colour images can be generated using near infrared, red, and green bands in what is known as a false colour composite (FCC) and does not look natural, like the image we see with the naked eye. For a trained interpreter, this does not pose any problems. However, when the intended use is a fly‐through of a draped terrain, visual interpretation, or a display, meant for the non‐remote sensing professional, this becomes a handicap. To overcome this, there is a requirement to generate natural colour composites (NCC) from the given false colour composite, which demands the simulation of a blue band to be combined with green and red bands. This paper describes a unique method of generating a blue band to form natural colour images from a given false colour image set. We use a spectral transformation method to establish a relationship between the false colour and true colour image pairs provided by a sensor with all the four bands, which has a broader spectral coverage. A transformation function is fitted by selecting radiometric control points along the line of geometric registration to find a set of coefficients to be used for simulating a blue band. This blue band, along with the green and red bands, provides a near true colour or ‘ natural colour ’ on the display. In this paper, we present a set of adjustable radiometric transformation coefficients to accommodate variation in spatial and dynamic range offered by sensors to generate natural colour. These coefficients seem to work on a large number of images of different seasons, provided similar spectral bands and terrain are used. The proposed ‘ natural colour generator ’ can be used in changing false colour images to natural colour images with the aim of ‘what you get is what you would have seen’. [ABSTRACT FROM AUTHOR]

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semi-automatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted. [ABSTRACT FROM AUTHOR]

This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts. [ABSTRACT FROM AUTHOR]

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods. [ABSTRACT FROM AUTHOR]

This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of the second-order pixel neighbourhood was modelled using Markov random fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothing at the class boundaries. The hybrid classifier was applied to advanced wide-field sensor (AWiFS) and linear imaging self-scanning sensor-III (LISS-III) images from a rural area in India. Validation was done with a LISS-IV image from the same area. The highest increase in accuracy among the adaptive functions was 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images. [ABSTRACT FROM PUBLISHER]

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance,Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results. [ABSTRACT FROM PUBLISHER]

Investigation of the diversity of a landscape implies finding appropriate measures coupling information on richness and equitability. Most of the papers dealing with remotely sensed images have relied on the richness of digital numbers (DNs) or on Shannon entropy or Pielou evenness indices for measuring their heterogeneity. Instead, based on ecological theory, we will show that rank–abundance diagrams may be profitably used in remote sensing to take into account both spectral richness and spectral equitability at the same time, by using a unique approach. After a theoretical introduction to the problem, we will empirically test the proposed method by extracting DN abundances derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image representing contrasting landscapes (test sites), plotting the relative abundance of each DN value versus its rank (rank–abundance diagrams) and interpreting statistically and ecologically the achieved results. We do not propose rank–abundance diagrams as a replacement of existing measures of spectral diversity, but as a parallel method to encompass (at the same time) both richness and evenness of remotely sensed images. [ABSTRACT FROM AUTHOR]

A generalized search scheme for automatic registration of level-1 data of multiresolution and multi-sensor remote-sensing data was proposed. The robustness and time efficiency of the automatic registration process is critically dependant on the search scheme for identification of control points. The proposed method consists of three levels of search ranging from coarser to finer. This process was found to be capable of registering images to sub-pixel level which are independent of rotation and scale variations, and also translation that vary by few metres to kilometres. In order to reduce the low pass effect due to multiple transformations involved in the multi-level registration process, a direct correspondence between the reference image and target image was established so that a single resampling needs to be applied. This correspondence also helps to generate products at any desired pixel size and to keep the original resolution intact. In this scheme mutual information (MI) is used as a similarity measure and a non-rigid transformation, thin plate spline (TPS), is used for achieving sub-pixel registration accuracies. MI is found to be better for identification of match points even for images that are radiometrically nonlinear. Unlike global transformation methods, use of non-rigid transformations such as TPS achieves sub-pixel accuracy in the moderate hilly regions as well as high hilly regions where relief displacements are high, provided sufficient number of control points are generated. However TPS transformation demands accurate control points. A robust method for detection of inaccurate control points was adopted and explained in the paper. The scheme was tested on a number of combinations of remote sensing data of the same resolution and different resolution datasets, namely Cartosat-1 with Liss-4 of Resourcesat-1, Landsat Thematic Mapper (TM) with IRS-1C/1D and Cartosat-1 with Enhanced Thematic Mapper (ETM). The results are presented along with accuracies achieved. [ABSTRACT FROM PUBLISHER]

Many research papers have reported problems with existing fusion techniques. The most significant problem is that fusion images produce spectral distortion when they retain high spatial resolution. Independent component analysis (ICA) can eliminate low-order and also high-order redundancy for data. According to statistical theory, the most important information from data is always included in the statistical characteristic of high order. Therefore, independent components (ICs) resulting from the transform of the ICA provide information on the data that is otherwise hidden in the large data set, and describe the essential structure of the data. For a colour or false colour composite image, the ICs represent the main body, spectral and spatial detail information. In this study, a novel remote sensing image fusion method based on ICA is presented. By replacing the main body IC of a multispectral image with the vector of a panchromatic (PAN) image, the new ICs contain both spatial information of the PAN image and spectral characteristics of the multispectral image. Then an inverse ICA transformation (IIT) is performed to attain the fusion image. IKONOS and Enhanced Thematic Mapper Plus (ETM + ) images are used to evaluate the proposed method and others (hue-saturation-value (HSV), principal component analysis and wavelets). The fusion results are compared graphically, visually and statistically, and show that the proposed method can retain spatial and spectral information simultaneously, and has a better balance between them. [ABSTRACT FROM AUTHOR]

In preparation for the launch of the next generation of geostationary satellites, considerable effort has been placed on developing new products and algorithms for operational purposes. In addition to satellite-based products and algorithms, satellite imagery can be used to evaluate numerical weather prediction models. Important first steps have already been undertaken to produce synthetic satellite imagery from numerical model output. By comparing synthetic imagery with observed imagery, model performance can be evaluated with a relatively new metric. In this paper, synthetic Geostationary Operational Environmental Satellite (GOES)-12 imagery was used to improve the two-moment prediction of pristine ice in the RAMS (Regional Atmospheric Modeling System) mesoscale model. A thunderstorm event that occurred on 27 June 2005 over the central plains of the USA was chosen for study. Synthetic GOES-12 3.9 μm imagery of RAMS output was compared with observed GOES-12 3.9 μm imagery. A discrepancy between brightness temperatures of two anvils of thunderstorms led to an improvement in the prediction of pristine ice number concentrations. After the model was re-run, subsequent synthetic GOES-12 3.9 μm imagery of one anvil exhibited an improvement compared with observed imagery. Brightness temperatures of the second anvil became too warm, an issue that may be related to model-specified cloud condensation nuclei (CCN) concentrations. This example highlights the potential importance of using synthetic imagery to evaluate numerical weather prediction models. [ABSTRACT FROM AUTHOR]