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The Persian Gulf is characterized by a huge number of offshore oil and gas platforms, large number of many oil terminals, and busy shipping lines for supporting oil transportation. In this regard, developing various methods for assessing the oil pollution of the Gulf, including remote sensing, is extremely important. For the first time, synthetic aperture radar (SAR) images of the European Sentinel-1A and Sentinel-1B satellites were used routinely to monitor the entire Gulf in 2017–2019. To effectively analyse the SAR images and their detected dark patches, a monitoring method based on manual detection and all contextual information available using web-GIS approach, was used. This involved the creation of a dedicated geoportal with oceanographic, physical – geographical, industrial and navigational information on the water basin, including the offshore oil and gas infrastructure. Using this approach, as well as data from automatic ship identification system, most oil spills were detected, recognized and mapped. The summary oil spill distribution maps and main statistical results of the monitoring are presented and discussed. It is shown that the main sources of oil pollution in the Persian Gulf (oil slicks/spills from 0.5 to about 700 km2) are (in decreasing order) oil production, oil transportation, and local shipping. [ABSTRACT FROM AUTHOR]

As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster response and mitigation efforts. Accurately defining the extent of floods is a problem for traditional flood mapping approaches, which emphasizes the vital need for modern technologies such as Synthetic Aperture Radar (SAR) imaging. Additionally, there is a need to develop computer-aided tools specifically designed for automatically identifying areas that are vulnerable to flooding using SAR data. Nonetheless, the lack of consistent large datasets presents a barrier that prevents these algorithms from progressing and being used in real-world scenarios. For this reason, the present study aims to develop a semi-supervised semantic segmentation algorithm for accurate flood region delineation in SAR data. In particular, the paper proposes labeling unannotated instances of data using a pseudo-label generation strategy. In order to accomplish this, the study suggests using a self-supervised trained teacher model to generate pseudo-labels and speed up the training procedure. The teacher model is then trained with a student model to efficiently extract features from the labeled data. Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. A comprehensive assessment conducted on publicly available datasets produces promising results. These results confirm the usefulness and possible relevance of the suggested methodology in enhancing efforts related to flood zone identification and management.

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A flood susceptibility assessment is crucial for identifying areas that are susceptible to flooding. This task usually uses models, but prior flood susceptibility assessment models focused on the frequency or duration of floods, not both. Integrating the frequency and duration of floods in susceptibility assessment could provide a more accurate picture of flood susceptibility. This study aimed to utilise and assess a novel integrated model that considers the frequency and duration of floods to categorise vulnerability/susceptibility zones. This study focuses on the multi-hazard zone between Cuddalore and Sirkazhi on the east coast of Tamil Nadu, India. Sentinel-1 A and RISAT-1 A Synthetic Aperture Radar (SAR) images were analysed using the Classification and Regression Tree (CART) classifier. Eight SAR images were used to study the persistence and temporal evolution of flooding over 49 days in 2015, along with multi-temporal datasets for 2015, 2018, and 2019. The classification of flood-susceptibility zones based on the frequency and duration of flooding yielded an accuracy of 0.87, whereas the integrated model scored 0.96 in all matrices. The hybrid integrated analysis provided a comprehensive understanding of the area's flooding system, identifying the southern part of the study area as the most susceptible. The proposed model recommends a frequency-duration-based approach to demarcate flood susceptibility zones and potentially improve flood susceptibility assessments and management strategies. [ABSTRACT FROM AUTHOR]

This study aims to develop models for estimating topsoil properties by analyzing both the parametric and textural features extracted from Sentinel-1 C-SAR (VV, VH) images. Field measurements were collected from 13 soil samples in the Laylan region of Kirkuk City, Iraq, and utilized to develop and validate the models. The study employed classification algorithms, including the random forest (RF) and maximum likelihood (ML) classifiers, using specific indicators derived from Sentinel-1 data. Additionally, a soil triangle was constructed using three axes to represent the predicted target parameters, facilitating the identification of five distinct soil groups in the study area. The findings reveal that the soil triangle enables the delineation of five subcategories of soil characterized by varying proportions of sand and silt. Each soil sample was categorized into one of five predefined classes based on its clay content, ranging from 0% to 14.48%. The performances of the ML and RF algorithms were assessed, demonstrating their effectiveness in estimating percentage labels despite limited training data, with ML exhibiting higher accuracy than RF. The developed models showed promising potential; however, their applicability should be tested across diverse geographic regions with varying climatic conditions. Future research could focus on utilizing these models to generate soil texture maps, potentially enhancing soil parameter estimation in different environments. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are associated with severe weather phenomena, making accurate wind field retrieval crucial for TC monitoring. SAR's high-resolution imaging capability provides detailed information for TC observation, and wind speed calculations require wind direction as prior information. Therefore, utilizing SAR images to retrieve TC wind fields is of significant importance. This study introduces a novel approach for retrieving wind direction from SAR images of TCs through the classification of TC sub-images. The method utilizes a transfer learning-based Inception V3 model to identify wind streaks (WSs) and rain bands in SAR images under TC conditions. For sub-images containing WSs, the Mexican-hat wavelet transform is applied, while for sub-images containing rain bands, an edge detection technique is used to locate the center of the TC eye and subsequently the tangent to the spiral rain bands is employed to determine the wind direction associated with the rain bands. Wind direction retrieval from 10 SAR TC images showed an RMSD of 19.52° and a correlation coefficient of 0.96 when compared with ECMWF and HRD observation wind directions, demonstrating satisfactory consistency and providing highly accurate TC wind directions. These results confirm the method's potential applications in TC wind direction retrieval. [ABSTRACT FROM AUTHOR]

The identification of ships in Synthetic Aperture Radar (SAR) imagery is critical for effective maritime surveillance. The advent of deep learning has significantly improved the accuracy of SAR ship classification and recognition. However, distinguishing features between different ship categories in SAR images remains a challenge, particularly as the number of categories increases. The key to achieving high recognition accuracy lies in effectively extracting and utilizing discriminative features. To address this, we propose DCN-MSFF-TR, a novel recognition model inspired by the Transformer encoder–decoder architecture. Our approach integrates a deformable convolutional module (DCN) within the backbone network to enhance feature extraction. Additionally, we introduce multi-scale self-attention processing from the Transformer into the feature hierarchy and fuse these representations at appropriate levels using a feature pyramid strategy. This enables each layer to leverage both its own information and synthesized features from other layers, enhancing feature representation. Extensive evaluations on the OpenSARShip-3-Complex and OpenSARShip-6-Complex datasets demonstrate the effectiveness of our method. DCN-MSFF-TR achieves average recognition accuracies of 78.1% and 66.7% on the three-class and six-class datasets, respectively, outperforming existing recognition models and showcasing its superior capability in accurately identifying ship categories in SAR images. [ABSTRACT FROM AUTHOR]

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects. [ABSTRACT FROM AUTHOR]

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained. [ABSTRACT FROM AUTHOR]

Urban planning has, in recent years, been significantly assisted by remote sensing data. The data and techniques that are used are very diverse and are available to government agencies as well as to private companies that are involved in planning urban and peri-urban areas. Synthetic aperture radar data are particularly important since they provide information on the geometric and electrical characteristics of ground objects and, at the same time, are unaffected by sunlight (day–night) and cloud cover. SAR data are usually combined with optical data (fusion) in order to increase the reliability of the terrain information. Most of the existing relative classification methods have been reviewed. New techniques that have been developed use decorrelation and interferometry to record changes on the Earth's surface. Texture-based features, such as Markov random fields and co-occurrence matrices, are employed, among others, for terrain classification. Furthermore, target geometrical features are used for the same purpose. Among the innovative works presented in this manuscript are those dealing with tomographic SAR imaging for creating digital elevation models in urban areas. Finally, tomographic techniques and digital elevation models can render three-dimensional representations for a much better understanding of the urban region. The above-mentioned sources of information are integrated into geographic information systems, making them more intelligent. In this work, most of the previous techniques and methods are reviewed, and selected papers are highlighted in order for the reader-researcher to have a complete picture of the use of SAR in urban planning. [ABSTRACT FROM AUTHOR]

Urban areas are experiencing rapid growth, accompanied by significant disorder in Lima Metropolitan area and many other cities in South America. Due to decades of uncontrolled construction practices, it is imperative to identify and better understand the types of informalities prevalent in these recent urban areas. Addressing this lack of information is crucial for implementing appropriate countermeasures and developing new policies that benefit the communities residing in such areas. It is worth noting that understanding disaster risk aligns with the first priority of the Sendai Framework for Disaster Risk Reduction. In this study, we propose the use of radar satellite imagery recorded by the Sentinel-1 constellation since 2017 to identify clusters of urban growth in Lima Metropolitan area. Then, the informal urban clusters can be depicted by visual inspection of the last recorded high-resolution optical image. With good spatial and temporal resolution, we identified 25 clusters informal areas. Among our findings, we observed that several of these clusters are situated in landfills comprised of construction and other waste, increasing their vulnerability to debris flow, landslides, and earthquakes. Additionally, we noted that some new urban areas mainly consist of temporarily empty houses, highlighting the feasibility of implementing countermeasures, such as relocations, in the early stages of informal occupation. These results underscore the significant contribution of satellite radar imagery in identifying recent informal urban areas.

Ship detection is a critical and challenging task in aerial images. Due to the special generation method of SAR images, they have unique characteristics. However, different from objects in natural images, ships in SAR images are often distributed with arbitrary orientations and dense distributions. Recently, key points-based anchor-free object detection algorithms have attracted the attention of quite a few researchers. To solve the task of oriented ship detection based on key points, in this paper, we propose an oriented object detection method based on coordinate system projection (CSProjection). In this work, we first detect the key point of the ship, namely, the center point, then establish a coordinate system with the object center point as the base point, and obtain a bounding box of the oriented object through the projection information of the object. Our method can effectively reduce the number of parameters applied to determine the oriented bounding box during training and decrease the network complexity. Experimental results on several SAR ship detection datasets, including SSDD, SRSDD-v1.0 and the optical remote sensing dataset HRSC2016, indicate that our method can compete with state-of-the-art algorithms for oriented ship detection, even those with more complex backbones. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model's lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks. [ABSTRACT FROM AUTHOR]

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model's accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR's accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable. [ABSTRACT FROM AUTHOR]

Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image interpretation, it often prioritizes recognition over computational efficiency and underutilizes SAR image prior information. Therefore, this paper proposes YOLOv5s-based ship detection in SAR images. Firstly, for comprehensive detection enhancement, we employ the lightweight YOLOv5s model as the baseline. Secondly, we introduce a sub-net into YOLOv5s, learning traditional features to augment ship feature representation of Constant False Alarm Rate (CFAR). Additionally, we attempt to incorporate frequency-domain information into the channel attention mechanism to further improve detection. Extensive experiments on the Ship Recognition and Detection Dataset (SRSDDv1.0) in complex SAR scenarios confirm our method's 68.04% detection accuracy and 60.25% recall, with a compact 18.51 M model size. Our network surpasses peers in mAP, F1 score, model size, and inference speed, displaying robustness across diverse complex scenes. [ABSTRACT FROM AUTHOR]

The Gulf of Suez region accommodates diverse activities, including oil exploration and production, recreational activities, and export and import ports. The Gulf region is exposed to pollution risks due to these interactions, with few research studies documenting these pollution cases. This research aimed to use Synthetic Aperture Radar (SAR) satellite data to detect and map all the oil pollution incidents within the geographical extent of the Gulf of Suez that occurred from 2017 to 2021, locating the most affected regions and possible sources of pollution. It enabled the detection and mapping of nearly 150 oil spill incidents that occurred over 67 dates during the study period and covered 851 km2 of the sea surface. The year 2018 recorded the greatest pollution area over the study period, with 201 km2. Along the Gulf coast, Suez, Ain Sokhna, and Ras Ghareb cities recorded the highest number of marine pollution incidents. The research also located seven sources of pollution that frequently discharge into the Gulf water without regulations. This research recommends implementing a real-time monitoring system for oil pollution to robustly detect any future oil incidents in these high-risk areas as quickly as possible and minimize their environmental impact.

The identification of ships in Synthetic Aperture Radar (SAR) imagery is critical for effective maritime surveillance. The advent of deep learning has significantly improved the accuracy of SAR ship classification and recognition. However, distinguishing features between different ship categories in SAR images remains a challenge, particularly as the number of categories increases. The key to achieving high recognition accuracy lies in effectively extracting and utilizing discriminative features. To address this, we propose DCN-MSFF-TR, a novel recognition model inspired by the Transformer encoder–decoder architecture. Our approach integrates a deformable convolutional module (DCN) within the backbone network to enhance feature extraction. Additionally, we introduce multi-scale self-attention processing from the Transformer into the feature hierarchy and fuse these representations at appropriate levels using a feature pyramid strategy. This enables each layer to leverage both its own information and synthesized features from other layers, enhancing feature representation. Extensive evaluations on the OpenSARShip-3-Complex and OpenSARShip-6-Complex datasets demonstrate the effectiveness of our method. DCN-MSFF-TR achieves average recognition accuracies of 78.1% and 66.7% on the three-class and six-class datasets, respectively, outperforming existing recognition models and showcasing its superior capability in accurately identifying ship categories in SAR images.

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

Object detection in SAR images has always been a topic of great interest in the field of deep learning. Early works commonly focus on improving performance on convolutional neural network frameworks. More recent works continue this path and introduce the attention mechanisms of Transformers for better semantic interpretation. However, these methods fail to treat the Transformer itself as a detection framework and, therefore, lack the development of various details that contribute to the state-of-the-art performance of Transformers. In this work, we first base our work on a fully multi-scale Transformer-based detection framework, DETR (DEtection TRansformer) to utilize its superior detection performance. Secondly, to acquire rotation-related attributes for better representation of SAR objects, an Orientation Enhancement Module (OEM) is proposed to facilitate the enhancement of rotation characteristics. Then, to enable learning of more effective and discriminative representations of foreground objects and background noises, a contrastive-loss-based GRC Loss is proposed to preserve patterns of both categories. Moreover, to not restrict comparisons exclusively to maritime objects, we have also developed an open-source labeled vehicle dataset. Finally, we evaluate both detection performance and generalization ability on two well-known ship datasets and our vehicle dataset. We demonstrated our method's superior performance and generalization ability on both datasets. [ABSTRACT FROM AUTHOR]

According to existing signatures for various kinds of land cover coming from different spectral bands, i.e., optical, thermal infrared and PolSAR, it is possible to infer about the land cover type having a single decision from each of the spectral bands. Fusing these decisions, it is possible to radically improve the reliability of the decision regarding each pixel, taking into consideration the correlation of the individual decisions of the specific pixel as well as additional information transferred from the pixels' neighborhood. Different remotely sensed data contribute their own information regarding the characteristics of the materials lying in each separate pixel. Hyperspectral and multispectral images give analytic information regarding the reflectance of each pixel in a very detailed manner. Thermal infrared images give valuable information regarding the temperature of the surface covered by each pixel, which is very important for recording thermal locations in urban regions. Finally, SAR data provide structural and electrical characteristics of each pixel. Combining information from some of these sources further improves the capability for reliable categorization of each pixel. The necessary mathematical background regarding pixel-based classification and decision fusion methods is analytically presented. [ABSTRACT FROM AUTHOR]

The interference of natural factors on the sea surface often results in a blurred background in Synthetic Aperture Radar (SAR) ship images, and the detection difficulty is further increased when different types of ships are densely docked together in nearshore scenes. To tackle these hurdles, this paper proposes a target detection model based on YOLOv5s, named YOLO-CLF. Initially, we constructed a Receptive Field Enhancement Module (RFEM) to improve the model's performance in handling blurred background images. Subsequently, considering the situation of dense multi-size ship images, we designed a Cross-Layer Fusion Feature Pyramid Network (CLF-FPN) to aggregate multi-scale features, thereby enhancing detection accuracy. Finally, we introduce a Normalized Wasserstein Distance (NWD) metric to replace the commonly used Intersection over Union (IoU) metric, aiming to improve the detection capability of small targets. Experimental findings show that the enhanced algorithm attains an Average Precision (AP50) of 98.2% and 90.4% on the SSDD and HRSID datasets, respectively, which is an increase of 1.3% and 2.2% compared to the baseline model YOLOv5s. Simultaneously, it has also achieved a significant performance advantage in comparison to some other models. [ABSTRACT FROM AUTHOR]

Results of upwelling research in the Black Sea, namely in the northeastern part of the sea, near the Tendrovskaya Spit and western coast of Crimea, and off the coast of Turkey, are presented. The results are based on the use of Earth remote sensing data, in particular the data from color scanners (MODIS, VIIRS, OLCI-A, and OLCI-B), infrared radiometers (TIRS and AVHRR), and SAR images from synthetic aperture radars. An integrated approach using almost exclusively remote sensing data makes it possible to fully characterize the observed upwellings in the Black Sea. In the active phase, upwelling, in addition to sea surface temperature (SST), is usually reflected in both the chlorophyll a (Chl a) concentration field and sea surface roughness field. In our cases, the duration of upwellings varied from 6 to 10 days; the SST differences in the upwelling zone reached 3–8°C; and Chl a concentrations were 5–6 times higher than the background values, being 0.5–0.7 mg/m3. The maximum SST anomalies up to 8°C were observed off the Turkish coast. The analysis revealed a clear relationship between areas of reduced SST in the upwelling zone, sea surface roughness, and Chl a concentration. It is shown that, in the case of using a complete set of remote sensing data, observing, monitoring, and studying upwelling does not present any fundamental difficulties. [ABSTRACT FROM AUTHOR]

The increasing imaging capability of spaceborne Synthetic Aperture Radar (SAR) systems makes a challenging task in matching slant-range (SR) SAR images acquired from different orbit and side-viewing directions. However, currently existing algorithms for the SR SAR images matching are unsatisfactory and the correct homologous points cannot be matched. In this work, a robust and adaptable high-precision method for matching flipped SAR images based on oriented descriptors is proposed. Firstly, the ratio gradient calculation based on Gabor odd filter is introduced. Secondly, the oriented descriptors are built to eliminate the diversity of feature-based descriptors of homologous points of flipped SR SAR images, enabling directly match the flipped SAR images. Finally, the feasibility and reliability of the proposed method are verified by the experimental results of simulated flipped SAR images and actual flipped SAR images, and two directional descriptors of the slave image are constructed to match non-flipped SAR images. The results show that the proposed method performs better in precision with the root mean square error (RMSE) less than 1 pixel, and the proposed oriented descriptors can match SR SAR images. [ABSTRACT FROM AUTHOR]

The Gulf of Suez region accommodates diverse activities, including oil exploration and production, recreational activities, and export and import ports. The Gulf region is exposed to pollution risks due to these interactions, with few research studies documenting these pollution cases. This research aimed to use Synthetic Aperture Radar (SAR) satellite data to detect and map all the oil pollution incidents within the geographical extent of the Gulf of Suez that occurred from 2017 to 2021, locating the most affected regions and possible sources of pollution. It enabled the detection and mapping of nearly 150 oil spill incidents that occurred over 67 dates during the study period and covered 851 km2 of the sea surface. The year 2018 recorded the greatest pollution area over the study period, with 201 km2. Along the Gulf coast, Suez, Ain Sokhna, and Ras Ghareb cities recorded the highest number of marine pollution incidents. The research also located seven sources of pollution that frequently discharge into the Gulf water without regulations. This research recommends implementing a real-time monitoring system for oil pollution to robustly detect any future oil incidents in these high-risk areas as quickly as possible and minimize their environmental impact. [ABSTRACT FROM AUTHOR]

This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data.. [ABSTRACT FROM AUTHOR]

The aim of this study is modeling the dependence of maximum velocity of the Trooz Glacier (Kyiv Peninsula, West Antarctica) on air temperature. For this purpose, we processed a time series of meteorological observations at the Akademik Vernadsky station and the ice flow velocity of the Trooz Glacier. The ice velocities were determined from the synthetic aperture radar images, acquired by the Sentinel-1 satellite, for the period from May 2015 to November 2022. The SAR images were processed in the SNAP (Sentinel Application Platform) program using the Offset Tracking method. As a result, 219 ice flow velocity maps were obtained. During the studied period, the maximum velocities varied from 2.64 m/day (August 19, 2015) to 4.05 m/day (April 18, 2020). A functional dependence between the temperature data from the Akademik Vernadsky station and the remotesensing data on the air temperature above the glacier’s surface was established. We combined the three parameters (time series of the maximum velocities of the glacial flow, remote temperature measurements above the glacier, and direct temperature measurements at the Akademik Vernadsky station) in a linear model. In order to increase the accuracy of the modeling, an a posteriori optimization was carried out. As a result, the average error in determining the maximum velocity of the glacier reduced from 23 cm/day to 17 cm/day.

This article shows the contribution of the Italy-Cuba collaboration and technology transfer project: “Strengthening the Cuban marine meteorological system” (Marine Surveillance) in the application of SAR technology in the surveillance of the seas around Cuba from the acquisition of SAR images from the CosmoSky-MED satellite constellation for 9 months; and training for digital processing through the IT tools of the SEonSE platform. With these new tools, 2,550 SAR images were processed with the SeonSE Engine computer tool, with statistics of the vessels and oil slicks found in the same period using the SeonSE Portal software. During this period, a significant number of oil slicks were found in the seas surrounding Cuba, which evidenced the existence of inappropriate conduct of dumping of hydrocarbons into the sea, and the importance of monitoring these bad practices in the future to combat them. These new features constitute a new approach in marine surveillance and an impact on the early warning system for marine oil pollution, since the system is capable of detecting slicks and sending notification to end users.

ABSTRACTThis study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data..

This paper introduces a novel edge detection operator called 'vision transformer-based Dexinet (ViT-DexiNet)' to address the challenges of detecting small objects in synthetic aperture radar (SAR) images. SAR images are typically impacted by strong multiplicative noise, making edge detection difficult. Existing traditional methods have limited spectral data preservation capabilities and often result in a loss of clarity and integrity of salient features in SAR images. The proposed ViT-DexiNet operator employs a series of interconnected layers to extract and refine salient edge features from SAR images. It utilizes a vision transformer self-attention layer to capture the pattern and structural details of image features crucial for determining edges. The extracted feature maps are then processed by the DexiNet architecture, which consists of a dense block, transfer block, and upsampling network. This architecture helps preserve edge information at different scales in deeper layers. The series of layered blocks generate edge maps, which are concatenated and averaged through smoothing to remove noise and enhance edge details in SAR images, resulting in a final high-quality edge map. To evaluate the proposed ViT-DexiNet method, both qualitative and quantitative analyses are conducted using standard edge detection operators such as Canny and Sobel. The empirical results demonstrate that the ViT-DexiNet surpasses baseline edge detection operators. The achieved values of proposed edge detection operator are 97.92%, 97.72%, 97.64% and 97.41%, respectively for the metrics accuracy, precision, recall and f1-score. The ViT-DexiNet offers high-quality edge maps, simplifying the interpretation of data for small object detection. Overall, the ViT-DexiNet method shows promise in overcoming the limitations of traditional approaches and improving the detection of edges in SAR images. [ABSTRACT FROM AUTHOR]

Drifting icebergs present significant navigational and operational risks in remote offshore regions, particularly along the East Coast of Canada. In such areas with harsh weather conditions, traditional methods of monitoring and assessing iceberg-related hazards, such as aerial reconnaissance and shore-based support, are often unfeasible. As a result, satellite-based monitoring using Synthetic Aperture Radar (SAR) imagery emerges as a practical solution for timely and remote iceberg classifications. We utilize the C-CORE/Statoil dataset, a labeled dataset containing both ship and iceberg instances. This dataset is derived from dual-polarized Sentinel-1. Our methodology combines state-of-the-art deep learning techniques with comprehensive feature selection. These features are coupled with machine learning algorithms (neural network, LightGBM, and CatBoost) to achieve accurate and efficient classification results. By utilizing quantitative features, we capture subtle patterns that enhance the model's discriminative capabilities. Through extensive experiments on the provided dataset, our approach achieves a remarkable accuracy of 95.4% and a log loss of 0.11 in distinguishing icebergs from ships in SAR images. The introduction of additional ship images from another dataset can further enhance both accuracy and log loss results to 96.1% and 0.09, respectively. [ABSTRACT FROM AUTHOR]

To reduce the number of ground control points required for block adjustment of unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) images, a robust block adjustment method is pro-posed. The proposed method aims to solve the problem of unstable adjustment solutions caused by the jittering of UAV SAR platforms. This method is based on the range Doppler model. By analyzing the correlation between the antenna phase center, velocity, and Doppler centroid of the seven orientation parameters, this method performs a block adjustment of UAV SAR images with three orientation parameters: the antenna phase center initial imaging moment, Doppler centroid, and proximity delay. UAV SAR images obtained from an area of $2\times $ 3 km in Dengfeng were used to conduct adjustment experiments. The results obtained using different orientation parameter set-tings verify the robustness and effectiveness of the proposed method. The adjustment results under various control-point layout plans indicate that a uniform layout plan can achieve an adjustment accuracy better than 1 m with a small number of control points.

In the field of multi-view SAR target location, the greater the difference in viewing angles, the higher the target location accuracy. However, this makes it difficult to match the same target between the SAR images with different viewing angles. The performance of traditional SAR image-matching algorithms will deteriorate or even fail to match the images correctly when the viewing angle is gradually increased. To solve this problem, a fast SAR matching method for the SAR images with large viewing angles based on inertial navigation information and neighborhood structure consensus (ININSC) is proposed in this paper. In this algorithm, the key targets are detected in the SAR images by using the maximum connected domain algorithm and the K-means clustering algorithm, and the connected domain centroid of the target is taken as a feature point. The approximate position of the key targets after the viewing angle change is found through inertial navigation information, and then accurate and fast matching is achieved by using the consensus of the neighborhood topological structure of the key targets. The measured data sets demonstrate that compared with traditional SAR image-matching algorithms, the proposed ININSC algorithm solves such a problem that SAR images cannot be accurately matched under the differences at large viewing angles and has better robustness and timeliness. [ABSTRACT FROM AUTHOR]

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Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth's surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases. [ABSTRACT FROM AUTHOR]

One of the most significant radar imaging types is Synthetic Aperture Radar (SAR), which is utilized in numerous fields and applications. Large size of SAR images, limited storage capacity and links with restricted capacity prompted the need to use compression techniques. The compression technique used in this paper is Compressed Sensing (CS) in the form of Multi-scale/multi-Resolution Kronecker Compressed Sensing (MRKCS). Detecting target existence in the received SAR images is a critical and challenging task. Enhancement through Single-IMage Super-Resolution (SIMSR) is a very good choice to enhance the decision making performance through reducing the error and false-alarm rates. The main objective of this work is to use a reliable compression-decompression technique by which high compression rates could be achieved, while retaining the data of interest. This is followed by an effective image enhancement technique in order to increase detectability from the received SAR images. [ABSTRACT FROM AUTHOR]

Internal solitary waves (ISWs) with features such as large amplitude, short period, and fast speed have great influence on underwater thermohaline structure, nutrient transport, and acoustic signal propagation. The characteristics of ISWs in hotspot areas have been revealed by satellite images combined with mooring observation. However, the ISWs in the Timor Sea, which is located in the outflow of the ITF, have not been studied yet and the characteristics are unrevealed. In this study, by employing the Synthetic Aperture Radar (SAR) images taken by the Sentinel-1 satellite from 2017 to 2022, the temporal and spatial distribution characteristics of ISWs in the Timor Sea are analyzed. The results show that most of ISWs appear in Bonaparte basin and its vicinity. The average wavelength of the ISWs is 248 m, and most of the wave lengths are less than 400 m. The peak line of ISWs is longer in deeper water. The underwater structures of two typical ISWs are reconstructed based on the Korteweg–de Vries (KdV) equation combined with mooring observation. This shows that, compared with the two-layer model, the continuous layered model is more suitable for reconstructing the underwater structures of ISWs. Further analysis shows that both the rough topography and the spring-neap tides contribute to the generation of ISWs in the Timor Sea. This study fills a gap in knowledge of ISWs in regional seas, such as the Timor Sea. [ABSTRACT FROM AUTHOR]

As one of the natural hazards that occur in some parts of the world, floods have devastating effects on people, the environment, and infrastructure. The management of this crisis can be effective in reducing severe financial and life losses. A critical aspect of managing this natural disaster is the accurate identification of flooded areas and their trends. The article presents a method for the identification and segmentation of flooded areas using the UNET++ neural network and Sentinel-1 satellite images in cband and single-polarized (HH and VV) and double-polarized (HH+HV and VV+VH) forms. These images were provided by NASA from Nebraska, Alabama, Bangladesh, Red River North, and Florence. The labeling process for all these images was done by the NASA implementation team and the IEEE GRSS Earth Science Informatics Technical Committee. In this network, EfficientNet-B7 is used as an encoder for feature extraction. Based on evaluation criteria such as IoU, F1-score, Recall, Accuracy, and Precision, the efficiency of the model has been evaluated. This model has demonstrated a high potential for detecting and segmenting flooded areas. Using this method, 84.77% IoU is obtained, which is higher than other methods such as UNet and FPN neural networks which participated in the ETCI (the maximum IoU obtained by these methods is 76.81%). [ABSTRACT FROM AUTHOR]

Radar images are commonly applied to recognize and monitor oil seeps on surface waters over continental shelves. In San Jorge Gulf, Patagonia, between 46° S and 48° S, oil slicks have been surveyed performing ellipse patterns in response to mesotidal dynamics. These effects were assigned to recent episodic increments of summer bottom temperatures at depths between 100 and 120 m, which are 2 °C warmer than those recorded during the 20th century. Slicks are assumed to have their origin from faults already known by the oil industry onshore. The effects here described should be envisaged in a climate-change scenario leading to temperature increases of the oceans’ shallow waters, together with other effects such as the human-induced global sea level rise. Under such warmer conditions seeps from continental shelf floors will become more frequent, and their contribution to the atmospheric C budget should be globally assessed.