Search

The article discusses the study on how far optical paper quality characterization can reach on its own merits. The study involves the use of a combination of digital image acquisition, followed by the angle measurement technique (AMT). Fourteen selected paper quality parameters were evaluated using new image analytical techniques for characterization and monitoring of paper quality. The results show that the new remote sensing image technique shows promising application potential for online product monitoring of the specified physical qualities.

This article introduces a special issue on hyperspectral remote sensing, which involves advanced satellite sensors that provide data in three categories: hyperspatial, superspectral, and hyperspectral. These data types offer higher resolutions but also present challenges in handling and analysis. The special issue aims to explore the characteristics and limitations of hyperspectral data and their opportunities for advancing remote sensing science. The selected papers in the issue cover various applications and use advanced techniques to provide valuable knowledge in the field. The document discusses three research papers related to remote sensing and hyperspectral data, focusing on mapping building shadows, identifying asphalt pavement aging, and separating wheat crops from similar crops. It emphasizes the potential of hyperspectral data in advancing remote sensing science while acknowledging the challenges associated with data analysis and calibration. [Extracted from the article]

The remote sensing satellite observation process involves multiple stakeholders and significant costs, so selecting an appropriate observation scheme and reaching an agreement on the chosen scheme among the evaluators/stakeholders is essential. From this perspective, the observation scheme selection problem can be viewed as a large-scale group decision-making (LSGDM) problem, challenging due to its complex group composition and the high consensus level required. Accordingly, this paper investigates an adaptive bi-directional consensus model that incorporates the evolution of social influence to address the LSGDM problem. Firstly, the dual-attribute affinity propagation algorithm is employed to divide the large-group into manageable subgroups. Secondly, the social influence evolution model is established, where evaluators' social influences are determined by considering their opinion similarity and trust level, and subgroups' social influences are updated by measuring their decision risk. Thirdly, the bi-directional feedback mechanism is designed to adaptively generate adjustment strategies corresponding to different scenarios based on the evolution model. Finally, an observation scheme selection case is analyzed using the proposal to demonstrate its practicality. During the process of remote sensing satellite observation, the selection of an appropriate observation scheme can optimize the utilization of existing satellite resources and ensure the quality of satellite observation services, thereby better meeting the demands of diverse application areas such as environmental monitoring, disaster management, and urban planning. [ABSTRACT FROM AUTHOR]

The real-time observation of surface temperature using remote satellite sensors is a critical parameter to analyse the greenhouse effect on the Earth. However, the transmission of satellite imaging in high resolution may cause high latency in geo-science applications to achieve real-time manner. In this paper. the deployment of deep learning architecture using Generative Adversarial Network (GAN) is applied to increase super-resolution reconstruction using low resolution imaging captured from the South China Sea sea surface temperature data. In addition, the development of spectral normalization is added to the Enhanced Super Resolution GAN (ESRGAN) architecture to improve the training mechanism of generator and discriminator. This improved ESRGAN is compared with its super resolution performance against peak signal-to-noise ratio and structural similarity index evaluation metrics. The experiment shows that the low resolution of South China Sea data can be inferred to obtain a higher resolution with a more realistic resolution as compared to the conventional upsumpling approaches. [ABSTRACT FROM AUTHOR]

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model's ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. [ABSTRACT FROM AUTHOR]

This paper represents a wide incident angle-polarization insensitive, stable in results, and compact-sized metamaterial absorber that can be used for Doppler navigation aids and remote sensing applications in Earth Exploration Satellite Services. The unit cell dimension was subwavelength-sized of 0.112λ X 0.112λ, with a good effective medium ratio (8.9), and achieved polarization insensitivity at a maximum incident angle up to 180°, making it more effective for proposed applications. The absorber resonated at 3.855 GHz, 5.13 GHz, 9.855 GHz, 13.335 GHz, and 16.02 GHz with single negative metamaterial properties due to negative permeability and absorptions of 99.7 %, 99.2 %, 98.6 %, 98.1 %, and 97.9 %, respectively. An RLC equivalent circuit on ADS was analyzed to validate the simulated results using the dipoles created by the surface currents. The absorber is cost-effective as no lumped elements or multilayer substrate materials were used, making fabricating less time-consuming and simple. The simulated results were validated by measuring data from a vector network analyzer after fabricating it. The proposed metamaterial absorber is suitable for use in Doppler navigation and Earth Exploration Satellite Services, and this research can further be extended to beamforming and sensing applications in the future. [Display omitted] • A novel maze-enclosed quad symmetric kite-shaped metamaterial absorber is proposed for quad band applications. • The absorber was designed with subwavelength size of 0.112λ X 0.112λ mm with maximum absorption of 99.7%. • It works perfectly for both co and cross-polarized waves and was justified by simplified equivalent circuit. • Absorptions with SNG features can be used to improve sensing, antenna gain, and EM coupling reduction. • This absorber is ideal for using with Earth Exploration Satellite Services and Doppler Navigation systems. [ABSTRACT FROM AUTHOR]

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods. [ABSTRACT FROM AUTHOR]

Despite the ongoing advancements in deep learning, challenges persist in the domain of change detection in remote sensing imagery. Objects with intricate structures and features may exhibit different shapes or appearances at different times or spatial locations. While most models aim to improve the performance of change detection tasks, these enhancements may lead to significantly increased computational efficiency. In this paper, we propose a global context enhancement network. Firstly, we use ResNet18 to extract dual-temporal features, which are then represented as concise semantic labels by an image semantic extractor. Subsequently, we process these semantic labels through a contextual transformer encoder to generate more refined remote sensing semantic labels enriched with abundant contextual information. The refined semantic labels are integrated with the original features and processed through a Transformer decoder to generate enhanced dual-temporal feature maps. Finally, through the processing of the classification head, we obtain pixel-level predictive images. Extensive experiments conducted on two public change detection datasets yielded impressive results, achieving an F1 score of 89.95% on the WHU-CD dataset and 95.16% on the SVCD dataset. When compared to state-of-the-art change detection models, our approach not only achieves significant performance gains but also maintains relatively high computational efficiency. Our method excels in capturing relevant features and relationships within input data, thereby enhancing the model's ability to represent relationships between different features. This results in a significant performance improvement without adding to the computational complexity. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of using the Sentinel-2 images for deriving bathymetric maps for the Nile River in Egypt. Regression analysis technique with the aid of in situ measurements were used. Exploring the effects of the size of calibration data, and water depth is another aim of this paper. Linear and machine learning regression techniques (Fine Decision Tree (FDT), and Random Forest (RF) Algorithms) are investigated. The study area is about 23 km between Assiut and Delta barrages. Around 82,000 depth points, are available. Regression models are developed using the depth data and the corresponding digital values of the 13 Sentinel-2 imagery bands. The linear regression model was not applicable (R2 = 0.02). The FDT, and RF models, have (R2 = 0.86) when half of the depth points were used for calibration, and the RMSE of the testing data (the other half of the depth points) equaled to 2.86 and 2.09 m, respectively. When fewer points were used for calibration, the R2 = 0.83 and 0.84, and RMSE = 2.65 and 2.19 m, for FDT and RF, respectively. When only the shallow water areas were considered, the RMSE reached 1.51 and 1.30 m for the FDT and RF, respectively. The VNIR bans of Sentinel-2 images are not enough to estimate the water depth of the Nile river. Collecting in situ measurements for small areas, is adequate for estimating the water depth and produces bathymetry maps for the Nile River in Egypt, and larger in situ measurements are not required. [ABSTRACT FROM AUTHOR]

In recent times there have been excess flooding due to climate change, which leads to disaster and destruction. Hence the aim of this paper is to investigate spatial impact of the two flood episodes that occurred in 2012 and 2022 along Lower Niger Basin in Nigeria using remote sensing data in GIS environment. Data used include administrative map of Nigeria, time series 30m resolution Landsat imageries captured before and during the peak of the flood episodes, SRTM DEM of 30m resolution and data generated from ground truthing. The pre-flood image was used as reference to determine the extent of flood in the disaster imageries. SRTM data was used for preparation of digital elevation model so as to investigate the influence of topography on spatial spread. This study revealed that 5622.13square kilometers of the study area (7.92%) was covered by flood in 2012 while 7428.771 square kilometers of land (10.47%) was inundated in 2022. This means that additional 1806.641 square kilometers (2.55%) of land surface has been exposed to flood in the study area within a time space of 10years. Upon field verification it was found that this was partly attributed to anthropogenic activities and urban expansion. It was therefore recommended that strict land use planning be enforced within the study area and public enlightenment programs should be initiated. [ABSTRACT FROM AUTHOR]

This paper explores, for the first time, a 405 km long wall system located in eastern Mongolia: the "Mongolian Arc" consists of an earthen wall, a trench, and 34 structures. It is part of a much larger system of walls built between the 11th and 13th centuries a.d. The Mongolian Arc, despite its magnitude, has been largely overlooked in existing academic discourse. Our team collected remote sensing data of different types and conducted an archaeological field survey of the entire Mongolian Arc. The different datasets obtained in the lab and the field were analyzed using a geographic information system (GIS). These results were integrated with excerpts from relevant primary sources to provide a preliminary interpretation of the design and potential functions of the Mongolian Arc. Key areas of exploration include the idiosyncratic gaps along the wall, the spatial organization of the wall and structures, and their interrelationship with the adjacent landscape. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation is a crucial technique in the field of land management. However, existing semantic segmentation networks require a large number of floating-point operations (FLOPs) and have long run times. In this paper, we propose a dual-path feature aggregation network (DPFANet) specifically designed for the low-latency operations required in land management applications. Firstly, we use four sets of spatially separable convolutions with varying dilation rates to extract spatial features. Additionally, we use an improved version of MobileNetV2 to extract semantic features. Furthermore, we use an asymmetric multi-scale fusion module and dual-path feature aggregation module to enhance feature extraction and fusion. Finally, a decoder is constructed to enable progressive up-sampling. Experimental results on the Potsdam data set and the Gaofen image data set (GID) demonstrate that DPFANet achieves overall accuracy of 92.2% and 89.3%, respectively. The FLOPs are 6.72 giga and the number of parameters is 2.067 million. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-scale information fusion based remote sensing small target detection method that aims to address the issue of low target detection accuracy in optical remote sensing images due to complex backgrounds, diversified scales, small targets, and different directions. Firstly, the architecture of the RepConv module significantly increases the detection accuracy of small targets without adding more inference time. Secondly, by introducing the ECA attention mechanism, a C3ECA module is constructed to effectively reduce the interference of complex background areas and achieve accurate positioning of the target area. The PANet structure in YOLOv5 is replaced by the BiFPN structure to balance the feature information of different scales and improve the detection performance of multi-scale objects. In addition, in order to solve the uncertainty of target direction and reduce the boundary discontinuity caused by angle regression, a circular smooth label method is used to provide an effective solution for target detection. The preprocessing method of image slices is employed to successfully achieve the target detection of high-resolution images. This approach greatly minimizes the issue of missed detection and erroneous detection of small objects in large images. The experimental findings indicate that the proposed approach markedly enhances the accuracy of remote sensing image detection and offers notable benefits in the recognition of small-scale targets. [ABSTRACT FROM AUTHOR]

Geospatial Data Analytics (GDA) is a futuristic platform for analyzing and processing volumetric data in remote sensing and GIS applications. GDA utilizes the Internet of Spatial Things (IoST), mist, fog, and cloud computing architecture as a backend tool for analyzing and processing big geospatial data. This paper introduces utilizing these interconnected network architectures of cloud, fog, and mist to process a large volume of geospatial data. Also, the paper presents a flexible, interconnected distributed network system, i.e., IoST-mist-fog-cloud GIS architecture, to analyze and manage geospatial data. The proposed system helps cloud platforms when MIST devices are trying to cut down on latency and boost throughput at the edge of the IoST tier. It also performs the geospatial crime data visualization of the total number of stolen vehicles from 2001 to 2011 from all the states of India as a case study by using the proposed model. It explains the mathematical and analytical queueing model of the proposed system. In addition, it performs a performance evaluation and experimental findings on the proposed architecture and uses graphs to represent the various arithmetic outcomes. The experimentation result proves the proposed interconnected network architecture's efficacy in terms of reliability and efficiency. [ABSTRACT FROM AUTHOR]

This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sensing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the application of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inexpensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty. [ABSTRACT FROM AUTHOR]

Reports on several recipients of the Leica Geosystems Award for Best Scientific Paper in Remote Sensing during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado. John Rogan; Jennifer Miller; Doug Stow.

Soil moisture is an important physical quantity that reflects the surface conditions. Soil moisture retrieval from remote sensing satellite monitoring data is a common method at present and the crucial issue is how to eliminate the influence of other surface and reflect soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic wave. In this paper, the Sentinel-1 SAR data, optical data and other auxiliary data such as the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration are used as data sources. By coupling the Advanced Integral equation (AIEM) model, a method suitable for the inversion of soil moisture content in the Duolun area is established, and the inverted soil moisture map is analyzed. [ABSTRACT FROM AUTHOR]

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [ABSTRACT FROM AUTHOR]

Abuja Municipal Area Council (AMAC) is experiencing rapid urban expansion, which is expected to impact land surface temperatures (LST). This paper evaluates the trends in soil adjusted vegetation index (SAVI), normalize difference buildup index (NDBI), and land surface temperature (LST) between 1987 and 2023 in AMAC using Landsat 4 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery, respectively. Results show that in 1987, SAVI ranged from -0.126 to 0.477, NDBI from -0.186 to 0.678, and LST from 27.18 to 46.4 °C. In 2023, SAVI ranged from -0.253 to 0.71, NDBI from -0.308 to 0.619, and LST from 23.89 to 46.57 °C. Analysis showed an increase in vegetation in 2023 compared to 1987. Built-up and bareland areas became more concentrated in the northeast in 2023 compared to 1987, and temperature reductions were observed in areas with increased vegetation, notably in the south and southwest. Correlation analysis indicated a strong negative relationship (-0.772) between SAVI and LST in 1987, weakening in 2023 (-0.389). NDBI and LST remained moderately positively correlated (0.645 in 1987, 0.621 in 2023). Significant differences (P<0.01) were observed between 1987 and 2023 SAVI, NDBI, and LST values. These findings have important implications for environmental monitoring, and urban planning in rapidly urbanizing areas such as AMAC. [ABSTRACT FROM AUTHOR]

A call for papers on remote sensing of global croplands and their water use for security of food in the Twenty-First Century for August 2012 special issue of journal "Photogrammetric Engineering & Remote Sensing," is presented.

Deep learning has been widely used in remote sensing image classification and achieves many excellent results. These methods are all based on relatively balanced data sets. However, in real-world scenarios, many data sets belong to the long-tailed distribution, resulting in poor performance. In view of the good performance of contrastive learning in long-tailed image classification, a new dualbranch fusion learning classification model is proposed to fuse the discriminative features of remote sensing images with spatial data, making full use of valuable image representation information in imbalance data. This paper also presents a hybrid loss, which solves the problem of poor discrimination of extracted features caused by large intra-class variation and inter-class ambiguity. Extended experiments on three long-tailed remote sensing image classification data sets demonstrate the advantages of the proposed dual-branch model based on contrastive learning in long-tailed image classification. [ABSTRACT FROM AUTHOR]

The Aquasense project is a multi-site Innovative Training Network (ITN) in water and food quality monitoring using autonomous sensors and intelligent data gathering and analysis. This paper aims to study the most suitable communication system model regarding radio signal coverage and networking performance in this frame. This objective is achieved by evaluating the expected performance of three long-range communications technologies, 4G/LTE, NB-IoT, and LoRa, to support a wireless sensor network reporting data from the sensing devices to a remote central station where they are collected and analyzed. In order for the performance to be assessed, the WinProp software suite is adopted to reproduce the communications scenarios and estimate the network throughput. The results clearly show that LTE outperforms the other two technologies as it achieves the highest throughput but is limited in terms of coverage. On the other hand, the LoRa and NB-IoT technologies achieve outstanding connectivity for large areas far away from the base stations, making the NB-IoT the solution offering the most valuable trade-of between connectivity and throughput. [ABSTRACT FROM AUTHOR]

Reports on several recipients of the John I. Davidson President's Award for Practical Papers during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado. Ross Nelson; Geoffrey Parker; Milton Hom.

Optical remote sensing is crucial in land management, maritime safety, and rescue operations. Currently, high resolution target detection faces the problems including feature loss, false detection, and limited network robustness. To tackle the aforementioned issues, this study introduces a novel MBS-NET model, which is built upon the YOLOv5 structure. The proposed model presents a multi-branch stacking module structure to precisely capture deep target feature information. By introducing a dual-channel attention mechanism module (EGCA) at the model's neck, the proposed method ensures discriminative feature acquisition in crowded objects and vast spatial scenes. Prior to network training, introduce an improved data augmentation strategy to accommodate the multi-scale and directional variations in remote sensing objectives for model detection. Experimental results on the large-scale public DIOR dataset demonstrate that the MBS-NET model introduced in this paper displays exceptional performance and remarkable interpretability in remote sensing scenarios at a large scale. MBS-NET model outperforms YOLOv5 and YOLOv7 models by increasing the accuracy by 5% and 2% respectively. In addition, the recall rate and F 1 Score index of MBS-NET model is superior to those of other methods, resulting in significant improvement of detection accuracy and robustness in large scenes. [ABSTRACT FROM AUTHOR]

Desertification leads to a decline in land production, resulting from biophysical interaction and human factors with temporal and spatial changes. This paper aims to evaluate the desertification sensitivity using Mediterranean Desertification and Land Use (MEDALUS) approach in the Northwestern El Minya Governorate in Egypt. The study area is one of the promising and important areas for land reclamation and developmental projects. Four indicators; climate, vegetation, management, and soil, were used in this study. Landsat 8 (OLI) satellite image 2021 was processed to calculate NDVI index to assess vegetation quality index. Then, it was classified into land cover classes to calculate the management quality index. ASTERDEM satellite data and the climate data were used in the climate quality index. Seventy soil samples were collected and analyzed for chemical and physical properties to be used for the retrieval of soil quality index. These indicators were integrated and used as input criteria in the model of desertification sensitivity. The physiographic unit extraction indicated that significant landforms were included in the study area namely; Old wadi Sediments, Peniplain, old river terraces, Shoulders, Sand sheets, Rock outcrops, and Sand dunes, Erosional Plains, Hill and Plain out wash. The obtained results showed that the severely sensitive area to desertification was represented by 86.7 % of the study area. The main factors affecting the sensitivity were climate conditions (arid, semi-arid), low soil quality and management in addition to the low vegetation cover. Furthermore, about 3% of the study area was located in the moderate sensitive class while the low sensitive area exhibits 1.06 %. Present study supports decision makers to reduce the constraining environmental conditions. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) radio occultation (RO) significantly impacts climate change monitoring. GNSS RO is based mainly on the refraction of the GNSS signals transmitted by satellites and received by a receiver on a Low Earth Orbit (LEO) satellite. Sentinel-6 (S6) is the first satellite from the Sentinel missions to retrieve atmospheric parameters using the RO technique. Temperature, pressure, and water vapor profiles can be retrieved using S6 based on the refraction of GNSS signals. This research evaluated the distribution and coverage of GNSS RO data from S6. This research also aims to evaluate the results of GNSS RO products, especially temperature from S6, from January 2022 to June 2022. The results of S6 are compared with the results from the Meteorological Operational Polar Satellite series (MetOp-B and MetOp-C) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset. Our results indicate that S6 allows for many RO observations in space by receiving signals from the Global Positioning System (GPS) and Russia's Global Navigation Satellite System (GLONASS). S6 provides monthly RO profiles from GPS that are greater than monthly RO profiles from GLONASS. Results show that S6 provides more GNSS RO data than MetOp-B and MetOp-C. The findings indicate that the distribution of GNSS RO events from S6 over the globe is uniform in longitude and non-uniform at latitude, with the minimum number at the poles. This paper makes a significant contribution to exploring the atmosphere with the S6 as a promising satellite for weather forecasting and monitoring climate change. [ABSTRACT FROM AUTHOR]

The idea of creating small spacecraft and constellations based on them, placed in low and medium earth orbits, has been formulated and widely used in the field of space systems development over the last two decades. To date, there are a large number of small satellites designed for remote sensing of the Earth. This paper describes the purpose and main technical characteristics of the small earth remote sensing satellite "AIST-2D". It also describes the progress of the flight tests and the results obtained with the few payloads. In addition, examples of the use of the received Earth remote sensing data to solve various problems are presented. Finally, conclusions are drawn on the interim results of the spacecraft's operation and the prospects for the development of the "AIST-2" platform are described. The small spacecraft "AIST-2D" created in the shortest possible time (less than three years) is an example of successful implementation of a project to create a modern multifunctional spacecraft for remote sensing of the Earth. The experience gained in the development, construction, experimental testing and operation of the "AIST-2D" spacecraft opens up opportunities for the creation of a series of spacecraft based on the "AIST-2" platform with minimal costs and short production times, which will allow us to count on the creation of highly efficient space monitoring systems in the foreseeable future. [ABSTRACT FROM AUTHOR]

Accurate and effective rice identification has great significance for the sustainable development of agricultural management and food security. This paper proposes an accurate rice identification method that can solve the confused problem between fragmented rice fields and the surroundings in complex surface areas. The spectral, temporal, and spatial features extracted from the created Sentinel-2 time series were integrated and collaboratively displayed in the form of visual images, and a convolutional neural network model embedded with integrated information was established to further mine the key information that distinguishes rice from other types. The results showed that the overall accuracy, precision, recall, and F1-score of the proposed method for rice identification reached 99.4%, 99.5%, 99.5%, and 99.5%, respectively, achieving a better performance than the support vector machine classifier. Therefore, the proposed method can effectively reduce the confusion between rice and other types and accurately extract rice distribution information under complex surface conditions. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification is a challenging task that involves automatically assigning labels to remote sensing images based on predefined categories. The inherent intra-class diversity and inter-class similarity of remote sensing images make it difficult for classification models to capture the discriminative key information necessary for accurate labeling, resulting in classification confusion. This paper proposes a novel method called Multidimensional Attention and Feature Enhancement (MA-FE) to address this issue. The proposed MA-FE method comprehensively captures essential features in different dimensions of channel and position through the Multidimensional Attention (MA) module, which integrates and combines the captured features. The Feature Enhancement (FE) module then amplifies the discriminative features to suppress the interference of useless information, thus improving the representation ability of the model. We conducted detailed experiments on three public remote sensing datasets and performed a comparative evaluation with multiple remote sensing scene classification methods proposed in recent years. The overall accuracies of the proposed MA-FE method on these datasets were 99.66%, 95.68%, and 93.21%, respectively. Our experimental results demonstrate that the proposed MA-FE method is more effective in extracting complex features in remote sensing images than other methods, thereby proving its effectiveness. [ABSTRACT FROM AUTHOR]

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments. Therefore, in this paper, a weed image analysis approach has been proposed for the system of weed classification. In this system, for preprocessing, a Homomorphic filter is exploited to diminish the environmental factors. While, for feature extraction, an adaptive feature extraction method is proposed that exploited edge detection. The proposed technique estimates the directions of the edges while accounting for non-maximum suppression. This method has several benefits, including its ease of use and ability to extend to other types of features. Typically, low-level details in the formof features are extracted to identify weeds, and additional techniques for detecting cultured weeds are utilized if necessary. In the processing of weed images, certain edges may be verified as a footstep function, and our technique may outperform other operators such as gradient operators. The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification. Finally, the features have been used in logistic regression for weed classification. The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains. The proposed approach attained weighted average recognition of 98.5% against the weed images dataset. Hence, it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments. [ABSTRACT FROM AUTHOR]

Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7% higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels. [ABSTRACT FROM AUTHOR]

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order to perform next steps in image processing. Remote sensing images usually have large size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detect objects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reduce the runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_PFC. The formal representation and details of two these procedures are presented in this paper. The experiments on satellite image and remote sensing image datasets are given to evaluate proposed model. Validity indices and time consuming are used to compare proposed model to picture fuzzy clustering model. The values of validity indices show that picture fuzzy clustering integrated to MapReduce gets better quality of segmentation than using picture fuzzy clustering only. Moreover, on two selected image datasets, the run time of MPFC model is much less than that of picture fuzzy clustering. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for man-made object extraction in remote sensing images. This paper focuses on the design and implementation of a system that allows a user to extract multiple objects such as buildings or roads from an input image without much user intervention. The framework includes five main stages: 1) Preprocessing Stage. 2) Extraction of Local energy features using edge information and Gabor filter followed by down sampling to reduce the redundant information. 3) Further reduction of the size of feature vectors using Wavelet decomposition. 4) Classification and recognition of man-made structures using Probabilistic Neural Network (PNN) 5) NDVI based post-classification refinement. Experiments are conducted on a dataset of 200 RS images. The proposed framework yields overall accuracy of 93%. Experimental results validate the effective performance of the suggested technique for extracting man-made objects from RS images. Compared with other methods; the proposed framework exhibits significantly improved accuracy results and computationally much more efficient. Most notably, it has a much smaller input size, which makes it more feasible in practical applications. [ABSTRACT FROM AUTHOR]

Twenty years ago, the Spectral Variation Hypothesis (SVH) was formulated as a means to link between different aspects of biodiversity and spatial patterns of spectral data (e.g. reflectance) measured from optical remote sensing. This hypothesis initially assumed a positive correlation between spatial variations computed from raster data and spatial variations in the environment, which would in turn correlate with species richness: following SVH, areas characterized by high spectral heterogeneity (SH) should be related to a higher number of available ecological niches, more likely to host a higher number of species when combined. The past decade has witnessed major evolution and progress both in terms of remotely sensed data available, techniques to analyze them, and ecological questions to be addressed. SVH has been tested in many contexts with a variety of remote sensing data, and this recent corpus highlighted potentials and pitfalls. The aim of this paper is to review and discuss recent methodological developments based on SVH, leading progress in ecological knowledge as well as conceptual uncertainties and limitations for the application of SVH to estimate different dimensions of biodiversity. In particular, we systematically review more than 130 publications and provide an overview of ecosystems, the different remote sensing data characteristics (i.e., spatial, spectral and temporal resolution), metrics, tools, and applications for which the SVH was tested and the strength of the association between SH and biodiversity metrics reported by each study. In conclusion, this paper serves as a guideline for researchers navigating the complexities of applying the SVH, offering insights into the current state of knowledge and future research possibilities in the field of biodiversity estimation by remote sensing data. • We reviewed and discussed recent methodological developments based on SVH. • Ecosystems, indices, data and approaches have been deeply revised. • The uncertainties related to the SVH have been thoroughly analyzed. • Future aspects and new prospective have been highlighted. [ABSTRACT FROM AUTHOR]

• The center of gravity of China's EPC is located in Luoyang City, Henan Province. • During the study period, China's EPC in general exhibits an east-high and west-low trend. • During the period 2000–2019, there are significant regional differences in the strength of the spatial linkage of EPC in China. • In 2000–2019, the decoupling status of EPC in china is of two types of decoupling: weak decoupling and expansive coupling. Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of "low in the west and high in the east". In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) are the real-time virtual representation of systems, communities, cities, or even human beings with the substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters. However, this potential remains largely unrecognized and poorly understood in disaster risk management. This study explores current achievements, existing challenges, and the untapped potential of DT in disaster risk management, and accordingly, proposes an improved Digital twin-based post-disaster risk management framework. This paper employs a systematic literature review approach focusing on digital post-disaster risk management twinning (DPRMT) and smart early warning systems derived from two databases: Scopus and Web of Science. After a screening process with exclusion criteria, the final analysis synthesizes findings from a selected set of 96 papers. The results revealed that previous studies are not beyond only providing general statements about DT. There is a need for diverse data collection methods, considering demographic and financial aspects, understanding social dynamics, employing dynamic models, recognizing interconnected systems, and giving due attention to the often-neglected recovery phase. This study proposes a comprehensive DPRMT concept framework leveraging decision-makers with a holistic and efficient approach that offers real-time, detailed, and data-driven modeling solutions to achieve insights into disaster-affected areas and communities. It is also helpful to optimize response planning, resource allocation, and scenario testing by capturing the dynamic and complex human behaviors and understanding interconnected systems and entities that are often overlooked in previous disaster risk management studies. [ABSTRACT FROM AUTHOR]

Diver-robot interaction is an exciting and recent field of study. There are different ways a diver and robot can interact, such as using tablets or detecting divers with cameras or sonars. A novel approach presented in this paper uses direct diver-robot communication. To facilitate communication for humans, we use typical diver gestures, which are transmitted to a robot using a wearable glove and acoustic communications. Following previous work by the University of Zagreb and the University of Auckland, a collaboration to control an autonomous underwater vehicle based on a wearable diver glove has been made possible through the EU Marine Robots project. Under this project, Trans-National Access trials allow Laboratory for Underwater Systems and Technologies, University of Zagreb, to offer its robots and infrastructure to external partners. Initial trials with the University of Auckland, which were planned to take place on site, were transformed into remote access trials. This paper reports on these challenging trials and collaboration given the distance and time zone difference. The key point is to demonstrate the possibility of having a diver remotely controlling a robot using typical gestures recognized by a wearable glove and transmitted via acoustic modems (and the Internet for the remote connection). [ABSTRACT FROM AUTHOR]

For remote sensing image object detection tasks in the small object feature, extraction ability is insufficient and difficult to locate, and other problems. This paper proposes an improved algorithm for small object detection in remote sensing images based on a window self-attention mechanism. On the basis of You Only Look Once (YOLO)v5s, a shallow feature extraction layer with four times downsampling is added to the feature fusion pyramid and the window self-attention mechanism is added to the Path Aggregation Network. Experiments show that the improved model obtained the Mean Average Precision (mAP) of 78.3% and 91.8% on the DIOR and Remote Sensing Object Detection public data sets with frames per second of 65 and 51, respectively. Compared with the basal YOLOv5s network, the mAP has improved by 5.8% and 3.3%, respectively. Compared with other object detection methods, the detection accuracy and real-time performance have been improved. [ABSTRACT FROM AUTHOR]

It is widely known that academic English is used for specific purposes in cross-cultural communication between scientists. Simultaneously, there is a shortage of teaching materials, leading to a demand for the development of such materials. A remote-sensing field was chosen for this study. This study describes the results of a corpus-based analysis of academic vocabulary in remote sensing articles. The research was conducted using corpus linguistics methods and distributive statistical analysis, and a corpus manager, Sketch Engine, was used as a tool to process a large amount of data. This study used a corpus compiled from academic papers published between 2020 and 2022. The frequency of lexical units was extracted to analyse the coverage of Academic Word List Sublist 1 in the corpus; keywords, multi-word units, and word formation were also analysed in this study. Units from two remote sensing glossaries were retrieved from the corpus to analyse how often they occurred in the corpus. Corpus linguistic methods and distributive statistical analysis proved effective in creating a discipline-specific shortlist that can be used by educators, ESP learners, and authors in the field of remote sensing. Despite the narrow field coverage of this study, the results obtained can be applied to general academic English vocabulary and to further research in the field of ESP. [ABSTRACT FROM AUTHOR]

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution. [ABSTRACT FROM AUTHOR]

The improvement of protected areas (PAs) is a global challenge for society. In 2017, China implemented the ambitious "Ecological Conservation Redline" (ECR) program, with the aim of increasing its PA estate and improving human welfare. Since its implementation, the ECR has attracted much attention from both researchers and managers. However, the understanding of the ECR by some researchers is still unclear, resulting in debates about its practicality and effectiveness. We therefore provide a clear description of the ECR in this paper. The ECR was proposed as a conservation theory in China in 2011 and was transformed into nationwide practice in 2017. Compared with existing PAs, the ECR expanded the conservation network by considering hotspots of ecological function and fragility beyond biodiversity, which was highlighted by the example of Sichuan Province, China. To balance the demands of conservation and development, a list of "positive" human activities is presented following negotiations between government and stakeholders. In addition, a remote sensing platform and an ecological compensation system have been applied to provide technological and economic support to the ECR. Finally, we make recommendations for improving the redline identification method, evaluating the effects of its implementation, broadening the funding sources for ecological compensation, and strengthening legal safeguarding. The advancement of the ECR provides a new model for environmental planning leading to a sustainable future. • China's Ecological Conservation Redline program aims to improve the protected area network. • The redline concept considers hotspots of ecological function and fragility beyond biodiversity. • This paper clarifies the program and its core principles. • Recommendations are made to enhance the redline identification method and its evaluation. • Ecological compensation funding and strengthening legal safeguarding are also necessary. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance. • A hyperchaotic system 2D Tent coupled infinite collapse map (2D-TICM) is proposed. • Lossy compression 2D CS and 2D PG-CD improves the computational efficiency. • Encryption step is integrated into JPEG-LS and compression to retain important information. • Dynamic 3D Latin cube is designed with chaos and multiple S boxes substitution are proposed. [ABSTRACT FROM AUTHOR]

Landscape design and planning can play a role in beautifying the human living environment while improving the ecological protection of plants. However, it is difficult because it involves ornamental, ecological, social and other factors. Remote sensing data can be acquired and analyzed from vegetation distribution, ecological environment, land use and other information to support rational planning and design of plant protection landscape. Deep learning technology provides a powerful tool for the comprehensive use of remote sensing data to support plant protection landscape design. In this paper, a multiple dense network (MP-DenseNet) model is constructed based on Densenet to accomplish the task of classifying the vegetation cover of remotely sensed data, which provides data support for the design and planning of plant protection landscapes. The algorithm proposed in this paper is competitive in comparison with several models. Using the MP-DenseNet model, this paper categorizes the vegetation cover of the study area and proposes an overall planning scheme that balances plant protection and regional sustainable development. • Constructing RSI vegetation classification model MP-DenseNet based on DenseNet. • The MP-DenseNet model is trained with RSI and verified for superior performance. • Development planning scenarios for the study area using the MP-DenseNet model. [ABSTRACT FROM AUTHOR]

Cloud pollution on remote sensing images seriously affects the actual use rate of remote sensing images. Therefore, cloud detection of remote sensing images is an indispensable part of image preprocessing and image availability screening. Aiming at the lack of short wave infrared and thermal infrared bands in ZY-3 high-resolution satellite images resulting in the poor detection effect, considering the obvious difference in geographic height between cloud and ground surface objects, this paper proposes a thick and thin cloud detection method combining spectral information and digital height model (DHM) based on multi-scale features-convolutional neural network (MF-CNN) model. To verify the importance of DHM height information in cloud detection of ZY-3 multi-angle remote sensing images, this paper implements cloud detection comparison of the data set with and without DHM height information based on the MF-CNN model. The experimental results show that the ZY-3 multi-angle image with DHM height information can effectively improve the confusion between highlighted surface and thin cloud, which also means the assistance of DHM height information can make up for the disadvantage of high-resolution image lacking short wave infrared and thermal infrared bands. [ABSTRACT FROM AUTHOR]

Object recognition in remote sensing is a well-developed problem in both scientific and industrial applications. However, the limitations in computational resources often make it impossible to build an effective deep neural network for the specific task even using pre-trained backbones. In the paper, we propose the lightweight approach for the automated ensembling of models for object recognition. The experimental studies are conducted for the set of remote sensing data from different open datasets. The results confirm the correctness, effectiveness, and practical applicability of the proposed approach. Implementation of all algorithms is available as an open-source tool. [ABSTRACT FROM AUTHOR]

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases. To improve application of SAR images, a management system for SAR images is proposed in this paper. The system provides trainable land cover classification module and display of SAR images on the map. Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images. The classifier is based on convolutional neural network structure. Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems. Thus, it is adopted to create each user's classifier. In our experiments, it is shown that the module works well with two different SAR datasets. With this system, SAR data and land cover classification results are managed and easily displayed. [ABSTRACT FROM AUTHOR]

Palembang City is divided into two regions by the Musi river, and there are 114 tributaries streamed within Palembang City, with 31.58% located in the Seberang Ilir region and 68.42% in the Seberang Ulu region. In the lowland topography of Palembang City, with most of the region below 5 m above mean sea level, flooding becomes repetitive. This paper examines the flooded area within the Bendung river basin using multi-temporal composite remote sensing radar data from Sentinel 1. Flood events in November 2018 and December 2021 were selected. The flood extent and affected populations will be calculated using Google Earth Engine (GEE) and Geographic Information System (GIS) application. In this study, flood extent was delineated and compared for different digital elevation models. It is found that the threshold value, the DEM, and the availability of radar data in conjunction with the flood event will better delineate the affected area to the flood. The DEM contributed a 37.5% to 83% difference in the delineated area calculation. The script in the GEE is customizable for different areas of interest and can be applied to different areas with adjustments to the threshold value that should be a minimum of 1.0 or higher. [ABSTRACT FROM AUTHOR]

The present paper explores the expansion and directions of urban growth in Zagazig city, a large city in the Nile Delta of Egypt. The paper assessments the spatial expansion and directions of the urban area for the years 1952, 1984, 2000, 2019. Besides, urban growth shapes were determined using the coefficient of the shape equation. Moreover, the Ordinary Least Squares (OLS) model was used to determine the impact of the spatial factors (roads network, waterways, and the no residential uses) on urban growth and its directions. Finally, a prediction of urban growth directions that could accommodate the population growth up to 2050 was identified using the rate of the growth equation. The year 2050 was chosen according to the urban development plan proposed by the government 2050 for the region. The urban growth expansion and directions revealed that the unsustainable trend of built-up expansion continued in this area. The built-up area increased 14,81 km2 during the period (1952–2019), resulting in a lack of original agricultural land, pare land. Otherwise, the western direction was also continued to occupy the urban distance directions in the entire period (1952–2019) with an increase of 32.3 percent in the city. The coefficient of the urban growth shape equation shows that the urban growth in the city took an unincorporated shape (stripe shape) in1952 recorded a 0,1 value. While the city was transformed to incorporate shape (circular and radiative shapes) during the years1984, 2000, and 2019 with recorded value 0,3. The OLS model results indicate that the most effective spatial factor on urban growth was the no-residential uses and roads network along the directions based on the Pearson coefficient recorded 0,8841, 0,8431 and the coefficient of determination R2 recorded 0,7816, 0,7108 respectively. The urban area directions of Zagazig city are expected to reach 34.8 percent, especially in the western direction by 2050. Furthermore, the urban distance directions are expected to rise 30.7 % also in the western direction. The study has confirmed that it is beneficial to identify current and future urban growth, urban growth shapes, and development that help regional planning authorities manage future urban growth and development following the area's carrying capacity. [ABSTRACT FROM AUTHOR]

The production of remote sensing products is very time-consuming, because it contains a large amount of remote sensing data to be processed. To increase the production efficiency of remote sensing products, a remote sensing processing job is usually divided into multiple tasks that are executed in parallel in the cluster system. So efficient task scheduling is the core problem of remote sensing products production system. This paper issued this problem and proposed a hybrid particle swarm optimization algorithm to improve the production efficiency of the remote sensing products. First, the task scheduling model of remote sensing products was established to minimize the maximum task execution time. Then, in the framework of particle swarm optimization, an inertia weight parameters adjustment strategy based on linear decreasing was adapted. For the solution obtained in each iteration, a local search procedure of task movement between the production nodes was employed to improve the quality of the solution. In addition, a probability acceptance rule was also introduced to allow the acceptance of poor solutions avoid trapping into local optimum prematurely. Finally, we carried out several experiments in the CloudSim simulation platform. The experimental results demonstrate that the proposed algorithm is effective and more competition when compared with the existing scheduling algorithms, such as ant colony optimization, tabu search and other classical scheduling algorithms. [ABSTRACT FROM AUTHOR]