Search

Over the past decade, domain adaptation (DA) algorithms have been proposed to address domain gap problems as they do not need any interpretation in the target domain. However, most existing efforts focus on scenarios with only one source domain and one target domain. In this article, we explore the scenario with one source domain and mixed multiple target domains for remote sensing applications and propose a new algorithm, named the two-stage adaptation network (TSAN). First, we utilize the adversarial learning approach to confuse the classifier to discriminate between the source domain and the whole mixed-multiple-target domain. Second, we adopt self-supervised learning to divide the mixed-multiple-target domain with automated generation of ``pseudo''-domain labels, which guides our network to learn intrinsic features of multiple target domains. Finally, these two steps are combined as an iterative procedure. We integrate a test dataset that includes five remote sensing datasets and ten classes. Our method achieves an average accuracy of 63.25% and 73.68% with two typical backbones, considerably outperforming other DA methods with an average accuracy improvement of 4.84%-20.19% and 9.06%-17.04%, respectively. Furthermore, we identify the negative transfer effect in existing mainstream DA methods in remote sensing image classification with multiple different domains.

Fusing hyperspectral image (HSI) and multispectral image (MSI) of high spatial resolution is typically utilized to obtain HSIs of high spatial resolution. However, the spatial quality of most existing methods is unsatisfactory due to the limited spatial resolution of an MSI. To further improve the spatial resolution of the fused HSI while keeping the spectral information well, we propose a new computational paradigm, named HyperFusion, which simultaneously fuses hyperspectral, multispectral, and panchromatic images (PAN). To achieve this goal, we first establish two data fidelity terms based on a physical observation that HSI and MSI can be treated as degraded versions of the fused HSI. Consequently, the spatial and spectral information from HSI and MSI can be well preserved. To efficiently transfer the spatial details of PAN into the fused HSI while keeping the spectral information well, we further construct a prior constraint from PAN based on the structural similarity. Meanwhile, we impose another low-rank prior constraint on the coefficient matrix to accurately describe latent characteristics of the HSI with high spatial resolution. By incorporating the aforementioned data fidelity terms and prior constraints, we finally formulate the objective as an optimization problem and utilize the alternative direction multiplier method to solve it efficiently. Comprehensive experiments on simulated and real datasets are carried out to demonstrate the superiority of HyperFusion over other state-of-the-arts in terms of visual quality and quantitative analysis. We also adopt a simulated experiment of vegetation coverage index analysis to verify the effectiveness of HyperFusion in remote sensing applications.

High-resolution synthetic aperture radar (SAR) satellites have become an important way to observe the earth. However, the geometric positioning accuracy of SAR satellite images across different times and spaces is an important factor affecting the realization of global remote sensing applications. In this study, a multimode hybrid geometric calibration method that incorporates an atmospheric propagation delay correction and that can be used to detect systematic errors affecting the geometric positioning accuracy of SAR satellites is described. The spatiotemporal variation reasons of geometric positioning error sources for spaceborne SAR are then analyzed. Finally, the stability of geometric positioning accuracy is evaluated using the described method on data extracted from Yaogan-13 (YG-13) SAR satellite images with long time series and multiple test areas in China. The results reveal that during the study period (2015-2017), the geometric positioning accuracy of the YG-13 SAR system was relatively stable and is better than 3 m regardless of the spatial distribution, after removal of systematic pulse-dependent slant range errors and atmospheric correction. Furthermore, the validation results provide a reference for the design of SAR satellite systems, the establishment of calibration periods, and quantitative remote sensing application.

Polarimetric synthetic aperture radar (SAR) has been extensively used in various remote sensing applications. In this article, a unified framework is designed to compare the classification performance of different polarimetric systems, which include quad-polarimetric (QP), compact-polarimetric (CP), and dual-polarimetric (DP). To avoid problems, such as the lack of uniform standards in feature extraction, the classification algorithm is directly based on the statistical characteristics of the coherency/covariance matrix and is implemented by extending the Wishart mixture model (WMM). The GF-3 data set in San Francisco and the AIRSAR agricultural data set in Flevoland are used in the experiment, and the following conclusions are generated. QP can achieve the highest classification accuracy in all classification tasks. When distinguishing three typical classes (water, urban, and vegetation) with very different scattering characteristics, the performance of different polarimetric systems is similar, and QP has only a slight advantage. For classification tasks of different classes with similar scattering characteristics, CP performs better in agricultural scenes, and the overall accuracy (OA) is only reduced by 3%-4% compared with QP. DP performs better in urban scenes, and OA is only reduced by 1%-3% compared with QP. These conclusions can provide guidance for future payloads' design and the choice of polarimetric operation mode for existing multi-polarimetric SAR systems to achieve the purpose of giving full play to the advantages of different polarimetric systems.

The widespread application of drones and associated miniaturization of imaging sensors has led to an explosion of remote sensing applications with very high spatial and spectral resolutions. Three dimensional (3-D) ultra-high resolution digital outcrop models created using drones and oblique imagery from ground-based sensors are now commonly used in the academic and industrial sectors. While the generation of spatially accurate models has been greatly facilitated by the development of com- puter vision tools such as Structure from Motion, correction of spectral attributes to achieve material reflectance measurements remains challenging. Following the development of a topograph- ical correction toolbox (mephysto), we now propose a series of new tools that can leverage the detailed geometry captured by digital outcrop models to correct for illumination effects caused by oblique viewing angles and the interaction of light with complex 3-D surfaces. This open source code is integrated into hylite, a python toolbox for the full 3-D processing and fusion of digital outcrop models with hyperspectral imaging data. We validate the performance of our novel method using a case study at an open pit mine in Tharsis, Spain, and demonstrate the importance of accurate illumination corrections for quantitative spectral analyses. Significantly, we show that commonly applied spectral analysis techniques can yield erroneous results for data corrected using current state of the art approaches. Our proposed method ameliorates many of the issues with these established approaches.

Extracting the flying airplane from the satellite video has received a great deal of attention in remote sensing applications. Nevertheless, the challenge arises in two aspects: the limited spatial resolution of space-borne imaging devices, and the small size of the airplane with low contrast to the background. This paper presents a novel spatially accurate and temporally consistent model for the foreground airplane extraction. Specifically, each frame is decomposed as a linear combination of the airplane and background with a kind of mixture ratios. This enables more accurate spatial representation of the small airplane of low resolution. Then, the airplanes from different frames obey the common mixture ratios based on the sub-pixel motion estimation. This enables the temporally consistent extraction through frames. The effectiveness of our method is demonstrated by the qualitative and quantitative evaluation on both synthetic and real data.

In this letter, we propose a remote sensing image matching method that is simple yet efficient to deal with different deformations. Inspired by the region growing strategy used in image segmentation, we integrate the motion consistency into the general region growing pipeline from a novel perspective. Specifically, we first obtain a subset with a high ratio inlier as the seed correspondence set. Then, to find more reliable correspondences, we formulate the motion consistency into the correspondence growing criterion, which is general to be suitable to many remote sensing applications. Extensive experimental results on the public available remote sensing data set show that our method achieves the best performance compared with state-of-the-art methods.

Improving the performance of nonlinear unmixing has become an active topic among the remote sensing applications. Usually, the noise levels of hyperspectral images (HSIs) vary with different bands. However, this fact is generally ignored and may, to some extent, result in a degradation of the unmixing results. Nonetheless, valuable spatial information that provides a great potential for improving the performance has seldom been considered in the current nonlinear unmixing. In this letter, we propose a novel kernel-based nonlinear unmixing model in which the band-wise noise characterization and the spatial relationships of HSIs are incorporated to solve the above problems. Firstly, the noise levels of different bands are estimated based on the results of superpixel segmentation, and then they are used to characterize the roles of different bands in the unmixing process. To exploit the spatial relationships in the superpixels, a regional l₁ -norm regularization is proposed and incorporated into the unmixing model. Experimental results on both synthetic and real hyperspectral datasets demonstrate the superiority of the proposed model compared to the state-of-the-art nonlinear unmixing methods.

Accurate detection of small and dim targets in infrared imagery is a crucial component in infrared search and track which has broad utility in military and remote sensing applications. Low-rank models have enjoyed state-of-the-art performance in infrared tracking applications, but many approaches underutilize dynamics information which has the potential to improve performance in challenging tracking scenarios. We present two algorithms, robust principal components analysis with patched unbalanced optimal transport (RPCA + PUOT) and robust alignment by sparse and low-rank with patched unbalanced optimal transport (RASL + PUOT), which incorporate optimal transport dynamics regularization and demonstrate improved performance on realistic data.

Hyperspectral image (HSI) has been an important valuable information source for many remote sensing applications due to its rich spectral information. However, limited by existing imaging system, the spatial resolution of HSI greatly limits its practical applications. In this paper, in order to deal with the challenge of feature extraction and super-resolution of HSI, a progressive split-merge super-resolution (PSMSR) framework is proposed to overcome the inherent resolution limitations, which apply a multi-level split strategy from the task level, spectral level and feature level. Within this framework, a gradient-guided group-attention network (GGAN) is designed to extract the spatial-spectral features and reconstruct realistic texture, where group attention block (GAB) aims to increase the distinguishing ability of spectral channels, and gradient information is fully fused in the reconstruction process to promote sharp edges and realistic texture. Compared with current state-of-the-art CNN-based SR methods, the proposed method achieves significant improvement in six evaluation metrics and visual quality on multiple and diverse scenes, and it obtains a higher classification accuracy in classification experiment and gets better SR results on real data, which proves that our method can effectively improve the spatial resolution while preserving the spectral correlation. In addition, a series of ablation experiments prove the effectiveness of each component of our method.

Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with a very high spatial resolution (VHR) but made it challenging to apply image coregistration, and many change detection methods are dependent on its accuracy. Two images of the same scene taken at different times or from different angles would introduce unregistered objects and the existence of both unregistered areas and actual changed areas would lower the performance of many change detection algorithms in unsupervised conditions. To alleviate the effect of unregistered objects in the paired images, we propose a novel change detection framework utilizing a special neural network architecture--Generative Adversarial Network (GAN) to generate many better coregistered images. In this article, we show that the GAN model can be trained upon a pair of images by using the proposed expanding strategy to create a training set and optimizing designed objective functions. The optimized GAN model would produce better coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly. Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure. Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.

Color consistency optimization for multiple images is a challenging problem in image mosaicking. To facilitate the global color optimization, existing approaches mainly use less flexible models, e.g., linear or gamma function, to eliminate the color differences between multiple images. However, these models often struggle to eliminate the color differences that existed in the local areas and preserve the image gradient information. To solve this problem, we creatively propose a novel color-correction model, which comprised a series of local grid linear models. This model is simple, but it is flexible enough to approximate a variety of complicated local color variations. To obtain the optimal model parameters for each image globally, a specific cost function that considers both color consistency and gradient preservation is designed and solved. The aim of our approach is to generate a composite image with visually consistent color. The original color information may be destroyed. Thus, this approach is unsuitable for the quantitative remote sensing applications. The experimental results on several challenging data sets show that the proposed approach outperforms state-of-the-art approaches in both visual quality and quantitative metrics.

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

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 establ...

Image matching is an essential process in the mosaicking of unmanned aerial vehicle (UAV) image sets. At present, the most popular methods establish putative correspondences mainly based on local features; however, some mismatch will occur due to the repetitive pattern if only the feature similarity is employed. In this case, we propose a geometric information-assisted image matching method for UAV images. As the feature matching is restricted in pairwise geometric grid cells, some unnecessary feature similarity calculations are avoided, which is of great help to shorten the matching time. Compared to other methods, the proposed method greatly improves the matching speed without the sacrifice of accuracy, which has a practical significance for real-time remote sensing applications.

The principle and procedure of coding a convolutional neural network (CNN) in terms of the spectral transmittance of a programmable optical filter are proposed and discussed. They exhibit an intrinsic link between the CNNs and the optical filters, which leads to a methodology by which optical imaging through such spectral transmittance can be seen as equivalent to the results of hyperspectral data numerically postprocessed by the CNN. In such a manner, hyperspectral data acquisition and CNN postprocessing can be implemented simultaneously by the physical process of optical imaging in a snapshot; thus, more intelligent, informative, and real-time optical detection and sensing in the remote sensing applications can be achieved.

Airborne radar carried on board unmanned aerial vehicles (UAVs) is serving as the harbinger of new remote sensing applications for security and rescue in inclement environments. The mobility and agility of UAVs, along with intelligent onboard sensors (cameras, acoustics, and radar), are more effective during the early stages of disaster response. The ability of radars to penetrate through objects and operate in low-visibility conditions enables the detection of occluded human subjects on and under debris when other sensing modalities fail. Recently, radars have been deployed on UAVs to measure minute human physiological parameters, such as respiratory and heart rates while sensing through clothing and building materials. Signal processing techniques are critical in enabling UAV-borne radars for human vital sign detection (VSD) in multiple operation modes. UAV radar interferometry provides valuable VSD in both hovering and flying motions. In the synthetic aperture radar (SAR) configuration, UAV-based VSD is available at a high spatial resolution. Novel radar configurations, such as in through-material sensing, UAV swarm, and tethered UAVs, are required to penetrate obstacles, facilitate multitasking, and allow for high endurance, respectively. This article provides an overview of the recent advances in UAV-borne VSD, with a focus on the deployment modes and processing methods.

The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes. However, accurate and fast semantic segmentation of high-resolution aerial images for remote sensing applications is still facing three challenges: the requirements for limited processing resources and low-latency operations based on aerial platforms, the balance between high accuracy and real-time efficiency for model performance, and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images. To address these issues, a lightweight and dual-path deep convolutional architecture, namely Aerial Bilateral Segmentation Network (Aerial-BiSeNet), is proposed to perform real-time segmentation on high-resolution aerial images with favorable accuracy. Specifically, inspired by the receptive field concept in human visual systems, Receptive Field Module (RFM) is proposed to encode rich multi-scale contextual information. Based on channel attention mechanism, two novel modules, called Feature Attention Module (FAM) and Channel Attention based Feature Fusion Module (CAFFM) respectively, are proposed to refine and combine features effectively to boost the model performance. Aerial-BiSeNet is evaluated on the Potsdam and Vaihingen datasets, where leading performance is reported compared with other state-of-the-art models, in terms of both accuracy and efficiency.

Synthetic aperture sonar processing is used to generate imagery for remote sensing applications such as environmental characterization and object detection. Images primarily represent the initial geometric response of acoustic scattering, but there are additional information embedded in the raw data that are not well-represented in images. For example, responses such as internal multiple scattering or elastic scattering are delayed in time, and they appear defocused in imagery. A complementary processing algorithm is presented that improves the focus of late acoustic scattering responses, which can potentially provide additional information about the object and aid data interpretability.

The creation of an information system for various facilities is crucial as it is a vital instrument for effective urban management, especially in the areas of monitoring the state of such facilities to ensure adequate maintenance, ease of access, etc. This paper focuses on how remote sensing and other modern techniques can aid in developing a buildings’ information system (BIS) for the University of Nigeria Enugu Campus. It comprised mapping the university environment, particularly the buildings within its boundary as well as developing a database containing the attributes of these buildings. Satellite images of the campus were downloaded and a map of buildings in the campus was developed through geo-referencing and digitizing. A database for the attribute information related to these buildings was also developed and finally the database was linked to the map. This project was carried out using application packages such as Microsoft Excel, QGIS 2.4.0, PostgreSQL 9.3.1, and a Hand-held global positioning system (GPS) receiver for ground-truthing. It is recommended that the accurate identification of buildings in the campus, especially in the residential quarters be improved by placement of number tags as this was a challenge encountered during the course of this project. Beyond this project, it is also of great essence to develop an information system for all other utilities on campus so as to achieve a harmonized and complete information system inventory for the university as it will greatly aid the management and maintenance of these utilities. Key words: Buildings’ information system (BIS), spatial data, attributes data, database management system (DBMS), geographic information system (GIS).

Remote sensing is an approach that collects information from a scene using airborne or spaceborne sensors. It has been widely used in various fields of earth observation and space exploration, including resource utilization, environmental monitoring, geological exploration, agricultural production, urban planning, and so on. Essentially, remote sensing technology can be recognized as multidisciplinary science and engineering to efficiently treat macro-observation issues. Remote sensing instruments have developed significantly during the last decade. Furthermore, the number of available sensors has increased significantly and their applications are much more widespread by remote sensing data being used in Google maps and social media applications in addition to more traditional environmental monitoring and land-use approaches. With the fast development of remote sensing techniques and platforms, the amount of data with higher spectral, spatial, temporal resolutions and multiple structures available from remote sensing systems is increasing at an extremely fast pace. This has posed serious challenges for efficient and scalable processing in a timely fashion to support various practical remote sensing applications [1] – [3] .

Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.

The optical images acquired from satellite platforms are commonly multiresolution images, and converting multiresolution satellite images into full higher-resolution (HR) images has been a critical technique for improving the image quality. In this study, we introduced the generative adversarial network (GAN) and proposed a new fusion GAN (FusGAN) approach for solving the remote sensing image fusion problem. Specifically, we developed a new adversarial training strategy: 1) downscaled multiresolution images are adopted for generative network (G-Net) training, and 2) the discriminative network (D-Net) is used to adversarially train the G-Net by discriminating whether the original multiresolution images have been fused well enough. To further improve the capability of the network, we structured our G-Net with residual dense blocks by combining state-of-the-art residual and dense connection ideas. Our proposed FusGAN approach is evaluated both visually and quantitatively on Sentinel-2 and Landsat Operational Land Imager (OLI) multiresolution images. As demonstrated by the results, the proposed FusGAN approach outperforms the selected benchmark methods and both perfectly preserves spectral information and reconstructs spatial information in image fusion. Considering the common resolution disparities among intra- and intersatellite images, the proposed FusGAN approach can contribute to the quality improvement of satellite images and thus improve remote sensing applications.

Satellites are widely used for remote sensing applications. High resolution images are used for different geographical applications. Using geographical objects or spatial objects for analysis became prevalent in the contemporary era. Many supervised classification techniques came into existence to have efficient classification of high-resolution imagery. There are many factors that may affect classification of geographic images. They include the presence of mixed objects, feature selection, size of training set and segmentation scale. When these factors are considered for a systematic mining of images with high resolution, it results in improved performance. Especially in agricultural environments, it is essential to have such study to ascertain which supervised learning mechanism can best deal with the factors aforementioned. An algorithm named Feature Subset Selection (FSS) is defined to enhance classification accuracy. Different classification techniques such as Support Vector Machine (SVM), Random Forest (RF), Naive Bayes, k-Nearest Neighbour (KNN), Adaboost.M1 and Decision Table (DT) are used for the empirical study with spatial data mining. Useful analysis of the techniques is made and thus this paper provides valuable insights on mining images of high spatial resolution in agricultural environments.

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

In this paper, we consider object classification and detection problems. We propose an algorithm that is effective from the point of view of computational complexity and memory consumption. The proposed algorithm can be successfully used as a basic tool for building different remote sensing systems which are, in general, installed on UAVs. The algorithm is based on the Viola–Jones method. It is shown in the paper, that the Viola–Jones method is the most preferable approach to detect objects on-board UAVs, because it needs the least amount of memory and the number of computational operations to solve the object detection problem. To ensure sufficient accuracy, we use a modified feature: rectangular Haar-like features, calculated over the magnitude of the image gradient. To increase computational efficiency, the L1 norm was used to calculate the magnitude of the image gradient. To train orientation-independent complex classifier we use a more generic decision tree form of complex classifier instead of a cascade scheme. All mentioned improvements were evaluated during detection of the following objects: the PSN-10 inflatable life raft (an example of an object that is detected during rescue operations using UAVs), oil tank storage (such kind of objects are usually detected during the inspection of industrial infrastructure), and aircraft on an area of hardstand. The performance of the trained detectors was estimated on real data (including data obtained during the rescue operation of the trawler Dalniy Vostok and a subset of real images from the DOTA dataset).

The paper considers a digital image processing method that provides efficient transmission, redistribution and storage of aerospace environmental monitoring videographic information. The requirement to increase the monitoring efficiency, if necessary, to carry out regular monitoring of changes in the image field of the observed object is especially relevant when describing spatially aligned sets of images obtained after multi-zone, multi-time and multi-polarization shooting. In many remote sensing applications, the processing efficiency reflects the degree of reduction in the volume of transmitted, described, analyzed and stored videographic information. The computational processing of digital images based on the coordinate survey scheme is presented. The method can be used to perform the procedure for decoding images in order to solve the problem of classifying objects of interest, analyzing images. Such characteristics of binary objects as a spatial boundary and a contour were used as deciphering signs. These features make it possible to describe the shape of an object, its geometric parameters and search for images with specific spatial structures. The processing method is realized by executing efficient algorithms for coding an image on a discrete grid by means of a chain code and spectral coding based on the fast discrete Hartley transform. The result of processing comes down to minimizing the number of such basic computational operations as multiplication, addition, transferring, as well as reducing the time and capacitive complexity of the program, and reducing the redundancy of the initial data. An example of an effective representation and description of a segmented image of an object is given. The proposed method makes it possible to expand the technical capabilities of more efficient transmission and processing of images for solving problems in the field of remote sensing.

Mapping the exact extent of urban areas is a critical prerequisite in many remote sensing applications, such as hazard evaluation and change detection. The usage of Synthetical Aperture Radar (SAR) data has gained popularity due to the unique characteristics of the backscattered radio signal from human-made targets. The Sentinel-1 (S1) constellation, with a global revisit time of 6–12 days in Interferometric Wide Swath (IW) mode and free and open access to the data, allows the development of new applications to monitor urban sites. However, S1 is rarely considered when fine resolution is required due to the large pixel size and the need for spatial averaging to obtain robust estimators. We propose a method to improve Sentinel-1 urban classification performance by exploiting one Multi-Spectral (MS) image acquired by Sentinel-2 (S2). MS data is used for tracing the precise natural boundaries in a scene through superpixels segmentation. A machine learning approach is then applied to interpret the thematic context of each segment from short temporal stacks of coregistered SAR data. We use a short sensing period (around two months), so rapid changes can be traces. The proposed fusion of S1 and S2 data was tested in the area of Milan (Italy), with a total accuracy of about 90%. The ability to follow high-resolution details in a mixed environment is demonstrated, opening the possibility of efficiently tracing the human footprint.

Very high resolution (VHR) satellite imagery and image processing algorithms allow for the development of remote sensing applications including multi-temporal classification, tracking of specific targets, multimedia data integration, ecosystem processes analysis, and land cover/land use (LULC) mapping. Classification algorithms are the primary source to generate LCLU maps. Since texture information is essential to generate LULC maps from VHR images, the object-based classification methods should be used instead of pixel-based methods. Also, in urban mapping, it is vital to select the appropriate classifier according to the type of land covers. Recently, in addition to machine learning algorithms, deep learning methods have also been used to classify VHR images. In this study, we compare the accuracy of convolutional neural network (CNN) algorithm with some machine learning methods, for classification of Pleiades satellite image with 50 cm spatial resolution. The results showed CNN algorithm has the highest classification accuracy when the training samples are increased. However, the difference between the classification accuracy of the CNN and relevance vector machine (RVM) models is not that significant so that one could use a more straightforward method with less training data rather than a complicated one with large volumes of data.

We deal with the problem of information fusion driven satellite remote sensing (RS) image/scene classification and propose a generic hallucination architecture considering that all the available sensor information is present during training while some of the image modalities may be absent while testing. It is well-known that different sensors are capable of capturing complementary information for a given geographical area, and a classification module incorporating information from all the sources are expected to produce an improved performance as compared to considering only a subset of the modalities. However, the classical classifier systems inherently require all the features used to train the module to be present for the test instances as well, which may not always be possible for typical remote sensing applications (say, disaster management). As a remedy, we provide a robust solution in terms of a hallucination module that can approximate the missing modalities from the available ones during the decision-making stage. In order to ensure better knowledge transfer during modality hallucination, we explicitly incorporate concepts of knowledge distillation for the purpose of exploring the privileged (side) information in our framework and subsequently introduce an intuitive modular training approach. The proposed network is evaluated extensively on a large-scale corpus of PAN-MS image pairs (scene recognition) as well as on a benchmark hyperspectral image dataset (image classification) where we follow different experimental scenarios and find that the proposed hallucination based module indeed is capable of capturing the multi-source information, albeit the explicit absence of some of the sensor information, and aid in improved scene characterization.

The fusion technique of the spectral bands captured by the sensors carried onboard satellites is one digital processing method for extracting information and detecting ground targets. Image fusion - also known as pan-sharpening-provides the necessary means to combine many images into a single composite image that is suitable in visual interpretation processes or in digital interpretation. The principal objective of this study is to find the best suitable algorithms for obtaining integrative information from several separate images in one combined image. Based on the above, a special software system was designed to implement and test the fusion methods used in remote sensing applications by selecting and applying a Shift Invariant Wavelet Transform (SIWT) method to the remote sensing images and then comparing with four other different image fusion algorithms. Two objective mathematical methods were also used to measure the amount of shared information obtained in the images resulting from the fusion, as well as using the visual and Near-Infrared images of the new Sentinel-2 European satellite for a part of Nineveh province as experimental images. The results showed a preference of the wavelet transform method over the other fusion methods for the remote sensing images.

Research and development of algorithms for processing impulse radio ultrawideband signals is a trending issue within remote sensing applications, personal area networks, and RF imaging among other areas. We have designed an SoC-based platform, conceived for scientific experimentation, with a fully modular and configurable design. Built with off-the-shelf components, our design achieves a configurable UWB-capable sampling rate through an equivalent-time sampling scheme. In this letter, we introduce the system architecture, its main interfaces, and the rationale behind each module implementation.

Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.

Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy and remote sensing applications, e.g., topography reconstruction and subsidence estimation. Phase unwrapping (PU) is one of the key procedures of InSAR signal processing. Artificial intelligence (AI) techniques have proven to be potentially powerful in many fields and have been introduced into the PU domain, achieving superior performance. In this article, we provide a comprehensive overview of AI-based PU techniques in InSAR. We survey the AI-based single-baseline (SB) PU methods and then review the AI techniques related to multibaseline (MB) PU. In addition, we show several experimental examples of these methods, from both simulated and real InSAR data sets, which gives readers an overview of AI-based PU processing's potential and limitations. It is our hope that this article will provide researchers with guidelines and inspiration to further enhance the development of AI-based PU.

Background: The Yagi-Uda antenna is a highly directive antenna used widely in many applications including pulsed Doppler radars to study the dynamics of the atmosphere. Yagi antennas configured in planar array configurations in phased array radars to achieve high peak powers to probe the atmosphere from troposphere. In this paper, a two-element Yagi-Uda antenna design is presented to investigate the ionospheric irregularities from the Gadanki Ionospheric Radar Interferometer. A new approach devised for the first time to design the two element, wide beam width tilted Yagi antenna, where folded dipole acts as active driver element and reflector as parasitic element. Methods: Several design techniques have been studied and new approach has been employed in designing the antenna and simulations have been carriedout and optimized the performance at 30 MHz with 14o tilt towards geometric north from vertical (zenith) direction for the maximum back scattered echo gain. Based on the design antenna has been fabricated and the system performance has been evaluated. Detailed validation methods have been listed to validate the parameters like reflection coefficient, gain, bandwidth and front-to-back ratio. Results: The antenna is designed and simulated results with 4NEC2 provided the optimized parameters before fabrication. The measured results indicate that the antenna has a gain of 5.65dBi and a reflection coefficient of -30 dB and these results are in close agreement with the simulation results. The band width obtained is about 2MHz is very good for the ionospheric remote sensing applications. The peak power handling capability upto 1kW shows the reliable system design for continuous and long term use of the system. Conclusion: Two element wide beam width 14o tilted Yagi-Uda antenna at 30MHz has been designed, simulated and optimized. Realized system performance validated to use for ionospheric radar remote sensing application. Details of the test methodologies are explained and the same have been executed to characterize the performance of the fabricated antenna with simulation results by measuring reflection coefficient, gain, radiation pattern. All the measured results have very close agreement with the simulation results and satisfy the design requirements to fit into 30 MHz radar antenna array for dedicated ionospheric probing. In future, we intended to carry out the radiation pattern simulation of the 20x8 phased array antennas to describe the overall radiation pattern.

Hyperspectral imaging has been rapidly developing over the past decade, and modern sensor technologies can cover large areas with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging is used effectively in numerous remote sensing applications such as precision agriculture, environmental monitoring, food analysis, and military applications requiring estimation of physical parameters of many complex surfaces and identifying visually similar materials with acceptable spectral signatures. The scope of fusion of the two images, one with high spatial content and the other with high spectral content, is to estimate one image with high spatial and spectral content. This paper presents a brief review of recent image resolution fusion algorithms, including deep learning techniques, for hyperspectral images. The need of high resolution panchromatic (pan) and multispectral (MS) images, lossless registration of images from multiple sources and point spread function (PSF) impose limitations for performing pan sharpening process. Hence the fusion is achieved using multispectral images instead of panchromatic images on hyperspectral images. It is essential to identify and reduce uncertainties in the image processing chain to improve image fusion enhancement. This paper also presents the current practices, problems, and prospects of hyperspectral image fusion. In addition, some important issues affecting fusion performance are discussed.

This paper focuses on spatial quality assessment of pan-sharpened imagery that contains valuable information of input images. Its aim is to show that fusion functions respond differently to different types of landscapes. It compares a quality assessment of an object-level procedure with that of a conventional pixel-level-based procedure which assigns uniform quality scores to all image pixels of pan-sharpened images. To do so, after performing a series of pan-sharpening evaluations, a weighted procedure for spatial quality assessments of pan-sharpening products, allocating spatially varying weight factors to the image pixels proportional to their level of spatial information content is proposed. All experiments are performed using five high-resolution image datasets using fusion products produced by three common pan-sharpening algorithms. The datasets are acquired from WorldView-2, QuickBird, and IKONOS. Experimental results show that the spatial distortion of fused images for the class vegetation cover exceeds that of man-made structures, reaching more than 4% in some cases. Our procedure can preclude illogical fidelity estimations occurring when pan-sharpened images contain different land covers. Since particular image structures are of high importance in remote sensing applications, our procedure provides a purpose-oriented estimation of the spatial quality for pan-sharpened images in comparison with conventional procedures.

Single image super-resolution (SISR) is to recover the high spatial resolution image from a single low spatial resolution one, which is a useful procedure for many remote sensing applications. Most previous convolutional neural network (CNN)-based methods adopt supervised learning. However, paired high-resolution and low-resolution remote sensing images are actually hard to acquire for supervised learning SR methods. To handle this problem, we propose a novel cycle convolutional neural network (Cycle-CNN). Our network consists of two generative CNNs for down-sampling and SR separately and can be trained with unpaired data. We perform comprehensive experiments on panchromatic and multispectral images of the GaoFen-2 satellite and the UC Merced land use data set. Experimental results indicate that our method achieves state-of-the-art CNN-based SR results and is robust against noise and blur in remote sensing images. Comprehensively considering super-resolved image quality and time costs, our proposed method outperforms the compared learning-based SISR approaches.

This article includes the phases of conceptualization and validation of a picosatellite prototype named Simple-2 for remote sensing activities using COTS (Commercial-Off-The-Shelf) components and the modular design methodology. To evaluate its performance and ensure the precision and accuracy of the measurements made by the satellite prototype, a methodology was designed and implemented for the characterization and qualification of CanSats (soda can satellites) through statistical tests and techniques of DoE (Design of Experiments) based on CubeSat aerospace standards and regulations, in the absence of official test procedures for these kinds of satellite form factor. For the above, two experimental units were used, and all the performance variables of the different satellite subsystems were discriminated. For the above, two experimental units were used, and all the performance variables of the different satellite subsystems were discriminated against. These were grouped according to the dependence of the treatments formulated in thermal and dynamic variables. For the tests of the first variables, a one-factor design was established using dependent samples on each of the treatments. Then, hypothesis tests were performed for equality of medians, using nonparametric analysis of the Kruskal-Wallis variance. Additionally, multivariate analysis of variance was carried out for nonparametric samples (nonparametric multivariate tests), and the application of post hoc multiple-range tests to identify the treatments that presented significant differences within a margin of acceptability. To know the dynamic response and ensure the structural integrity of the satellite module, shock, oscillation, and sinusoidal tests were applied through a shaker. Having applied the experimental methodology to the different units, the results of a real experiment are illustrated in which a high-altitude balloon was used through the application of nonparametric regression methods. This experiment’s interest measured thermodynamic variables and the concentration of pollutants in the stratosphere to corroborate the operating ranges planned in the above experiments using on-flight conditions and estimate the TLR (technology readiness level) of future prototypes.

Super-resolution (SR) has received extensive attention in recent years for satellite image processing in a wide range of application scenarios, such as land classification, identification of changes, the discovery of resources, etc. Satellite images from satellite sensors are mostly low-resolution (LR) images, so they do not completely fulfill object detection and analysis criteria. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. We proposed a transferred wide residual Single Image Super-Resolution (SISR) remote sensing deep neural network model (WRSR). By increasing the width and reducing the residual network depth, the proposed approach has dramatically reduced memory costs. As a result, our model reduced memory costs by 21% in Enhanced Deep Residual Super-Resolution (EDSR) and 34% in SRResNet as a direct consequence of the in-depth reduction. The proposed architecture improves the efficiency of training loss by performing weight normalization instead of augmentation technology. We compared our method to five recent existing super-resolution (SR) deep neural network methods, tested over three public satellite image datasets and a standard reference (PRIM) dataset. Experiment analysis is evaluated in peak to signal noise ratio (PSNR) and structural similarity index measure (SSIM).

Multisensor Earth observation has significantly accelerated the development of multisensor collaborative remote sensing applications such as all-weather mapping using synthetic aperture radar (SAR) images and optical images. However, in the real-world application scenarios, not all data sources may be available, namely, the missing-modality problem, e.g., the poor imaging conditions obstruct the optical sensors, and only SAR images are available for the mapping. This real-world scenario raises the challenge of how to leverage historical multisensor data to improve the representation ability of the available model. The knowledge transfer based and knowledge distillation based approaches, as feasible solutions, can be used to transfer knowledge from other sensor models to the available model. However, these approaches suffer from the problem of forgotten knowledge and the multi-modality co-registration problem, which means that the leveraging of historical multisensor data is inefficient. The essential problem lies in the fact that these approaches are designed following the single-sensor data-driven approach. In this paper, a registration-free multisensor data-driven learning method, namely, deep multisensor learning, in a new perspective of knowledge retention, is proposed to overcome the above problems by learning a meta-sensory representation. To explore the existence of the meta-sensory representation, the meta-sensory representation hypothesis is first proposed, which reveals that the essential difference of the deep models trained on data from different sensors lies in the parameter distribution of the sensor-invariant and sensor-specific operations. Based on this hypothesis, a prototype network is proposed to learn the meta-sensory representation by modeling the knowledge retention mechanism, using the proposed difference alignment operation (DiffAlignOp). DiffAlignOp enables the prototype network to dynamically generate sensor-specific networks to gather supervised signals from registration-free multisensor data. This dynamic network generation is differentiable. Therefore, multisensor gradients can be obtained to learn the meta-sensory representation. To demonstrate the flexibility and practicality of deep multisensor learning, the application of all-weather mapping in a missing-modality scenario was performed. The experiments were conducted on a large public multisensor all-weather mapping dataset, which consists of high-resolution optical and SAR imagery with a spatial resolution of 0.5 m. The experimental results suggest that deep multisensor learning is superior to the other learning approaches in performance and stability, and reveals the importance of meta-sensory representation in multisensor remote sensing applications.

Anomaly detection is a key problem in hyperspectral image (HSI) analysis with important remote sensing applications. Traditional methods for hyperspectral anomaly detection are mostly based on the distinctive statistical features of the HSIs. However, the anomaly-detection performance of these methods has been negatively impacted by two major limitations: 1) failure to consider the spatial pixel correlation and the ground-object correlation and 2) the existence of the mixing pixels caused by both lower spatial resolution and higher spectral resolution, which leads to higher false-alarm rates. In this article, these two problems are largely solved through a novel hyperspectral anomaly-detection method based on image super-resolution (SR) and spatial correlation. The proposed method encompasses two innovative ideas. First, based on the spectral variability in the anomaly targets, an extended linear mixing model can be obtained with more accurate ground-object information. Then, image SR is used to improve the spatial resolution of the HSIs by injecting the ground-object information from the mixing model. This alleviates the effect of mixed pixels on anomaly detection. Second, spatial correlation is exploited jointly with the global Reed-Xiaoli (GRX) method and the ground-object correlation detection for anomaly detection. Experimental results show that the proposed method not only effectively improves the hyperspectral spatial resolution and reduces the false-alarm rate but also increases the detectability with the spatial correlation information. Furthermore, the results for the real HSIs demonstrate that the proposed method achieves higher rates of anomaly detection with lower false-alarm rates.

Anomaly detection in hyperspectral imagery has been an active topic among the remote sensing applications. It aims at identifying anomalous targets with different spectra from their surrounding background. Therefore, an effective detector should be able to distinguish the anomalies, especially for the weak ones, from the background and noise. In this article, we propose a novel method for hyperspectral anomaly detection based on total variation (TV) and sparsity regularized decomposition model. This model decomposes the hyperspectral imagery into three components: background, anomaly, and noise. In order to distinguish effectively these components, a union dictionary consisting of both background and potential anomalous atoms is utilized to represent the background and anomalies, respectively. Moreover, the TV and the sparsity-inducing regularizations are incorporated to facilitate the separation. Besides, we present a new strategy for constructing the union dictionary with the density peak-based clustering. The proposed detector is evaluated on both simulated and real hyperspectral data sets and the experimental results demonstrate its superiority compared with several traditional and state-of-the-art anomaly detectors.

With the initial establishment of global earth observation system in various countries, more and more high-resolution remote sensing data of multisource, multitemporal, multiscale, and different types of satellites are obtained. It is urgent to explore the advanced basic theory of remote sensing information science, design high-performance generic key technologies of remote sensing information system and global positioning system, and study complex engineering system of remote sensing applications and geographic information system. In this article, the basic theory exploration, inversion technology research, and engineering application design and development of generic optical remote sensing product (ORSP) are systematically reviewed. We classify the ORSP scientifically, review the main algorithms and application scope of 16 kinds of generic ORSP, and expound the validation and quality evaluation methods of ORSP in engineering application. Furthermore, we analyze the current core problems and solutions, and prospects for the state-of-the-art research and the future development trend of generic ORSP. This will provide valuable reference for scientific research and construction of high-resolution earth observation system.

Speckle reduction is a key step in many remote sensing applications. By strongly affecting synthetic aperture radar (SAR) images, it makes them difficult to analyse. Due to the difficulty to model the spatial correlation of speckle, a deep learning algorithm with self-supervision is proposed in this paper: SAR2SAR. Multi-temporal time series are leveraged and the neural network learns to restore SAR images by only looking at noisy acquisitions. To this purpose, the recently proposed noise2noise framework has been employed. The strategy to adapt it to SAR despeckling is presented, based on a compensation of temporal changes and a loss function adapted to the statistics of speckle. A study with synthetic speckle noise is presented to compare the performances of the proposed method with other state-of-the-art filters. Then, results on real images are discussed, to show the potential of the proposed algorithm. The code is made available to allow testing and reproducible research in this field., The manuscript is the accepted version of IEEE STARS. Code is made available at https://gitlab.telecom-paris.fr/RING/SAR2SAR

A georeferenced orthophoto built from aerial images is the basic resource for various of remote sensing applications. As traditional low flight altitude aerial photographic survey is hard to handle large areas, higher altitude brings better survey efficiency, and a telephoto camera is required to maintain the resolution. Since existed structure from motion (SfM) methods are not suitable to process the low field-of-view images with high altitude, this article presents a novel georeferenced orthophoto stitching solution for telephoto images, which contain both translation and rotation movements. A mission planning strategy is designed to guarantee the consistent quality, according to the flight plan and camera intrinsic parameters. The potential matching topology is obtained by projecting images to the prior ground plan,e and overlap scores are estimated for neighbors query, followed with a feature-based keypoint matching. The ground plane and camera poses are optimized by joint considering feature matches, GPS and gimbal rotation information, while the final orthophoto is fused with a fast exposure leveling and weighted multiband-blending algorithm. The experiments show that our system is able to output novel quality orthophoto with high robustness, and we shared a trial version of our algorithm.1

With the rapid development of high-resolution earth observation systems, the data processing, algorithm design, and system development of remote sensing spatial-temporal big data (RS-STBD) have gradually become the bottleneck problems in the application and development of earth observation system. The research on the model, algorithm, and system of RS-STBD processing involves complex scientific problems, technical bottlenecks, and inconstant requirements of engineering applications. This article summarizes the data type and processing theory model of RS-STBD, the high-performance algorithm design based on cloud service and intelligent computing, and the architecture design and engineering development methods of the complex remote sensing application system. Furthermore, the existing problems in the current research are analyzed, and the related solutions are given. Finally, the future development trend of scientific exploration, technical research, and application development of RS-STBD has prospected.

Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

The classification of tree species through remote sensing data is of great significance to monitoring forest disturbances, biodiversity assessment, and carbon estimation. The dense time series and a wide swath of Sentinel-2 data provided the opportunity to map tree species accurately and in a timely manner over a large area. Many current studies have applied machine learning (ML) algorithms combined with Sentinel-2 images to classify tree species, but it is still unclear, which algorithm is more effective in the automotive extraction of tree species. In this study, five ML algorithms were compared to identify the composition of tree species with multitemporal Sentinel-2 images in the JianShe forest farm, Northeast China. Three major types of deep neural networks [Conv1D, AlexNet, and long short-term memory (LSTM)] were tested to classify Sentinel-2 time series, which represent three disparate but effective strategies to apply sequential data. The other two models are support vector machine (SVM) and random forest (RF), which are renowned for extensive adoption and high performance for various remote sensing applications. The results show that the overall accuracy of neural network models is better than that of SVM and RF. The Conv1D model had the highest classification accuracy (84.19%), followed by the LSTM model (81.52%), and the AlexNet model (76.02%). For non-neural network models, RF's classification accuracy (79.04%) is higher than that of SVM (72.79%), but lower than that of Conv1D and LSTM. Therefore, the deep neural networks combined with multitemporal Sentinel-2 images can efficiently improve the accuracy of tree species classification.

The comprehensive utilization of images from various satellite sensors can significantly increase the performance of remote sensing applications and has, therefore, attracted extensive research attention. One of the essential challenges that research encounters comes from multisource image registration. This article proposes a novel region-based image registration method for multisource images. The proposed method exploits the region features of input images, which provide more consistent and common information of the multisource data. The image region features are extracted based on image semantic segmentation using the deep convolutional neural network approach. The final registration result is a pixel-level output corresponding to the input images. The proposed registration scheme overcomes the limits of traditional feature extraction methods (e.g., point feature) adopted in previous registration schemes. Results indicate that the proposed method has good performance for the multisource remote sensing image registration and can serve as a building block for the fusion of multisource images.