Synthetic Aperture Radar Datasets Research Articles

Conditional Generative Adversarial Networks for SAR-based Ocean Feature Monitoring

Ocean surface monitoring, detection of oil spills, and accurate maritime vehicle detection is an important set of problem statements associated with ocean pollution monitoring. This paper proposes a novel study on Pix2Pix or generative adversarial networks for the aforementioned task. A comparative analysis is presented on various convolutional network-based encoders for semantic segmentation. A publicly available Synthetic Aperture Radar (SAR) dataset from the Sentinel-1 C-band sensor is used, constituting a total of 1112 images for the task of multiclass segmentation. Each integrant image pixel is classified into five classes, Oil Spills, Ships, Look-Alikes, Land Surface, and Ocean Surface. Deep architectures like U-Nets, SegNets, LinkNets, and FCNs are thoroughly assessed to obtain a practical analysis of this approach. Through a systematic exploration of hyperparameters, we identified the best-performing encoder configuration, achieving impressive results with a weighted Mean IoU of 0.898 and an F1-score of 0.931. These findings hold great promise for the advancement of solutions in ocean pollution monitoring, contributing to more accurate and efficient detection and mitigation efforts. respectively.

Coniferous Forests Aboveground Biomass Inversion in Typical Regions of China with Joint Sentinel-1 and Sentinel-2 Remote Sensing Data Supported by Different Feature Optimizing Algorithms

Multispectral remote sensing (RS) data and synthetic aperture radar (SAR) data can provide horizontal and vertical information about forest AGB under different stand conditions. With the abundance of RS features extracted from multispectral and SAR datasets, a key point for accurate forest AGB estimation is to use suitable feature optimization inversion algorithms. In this study, feature optimization inversion algorithms including multiple linear stepwise regression (MLSR), K-nearest neighbor with fast iterative feature selection (KNN-FIFS), and random forest (RF) were explored, with a total of 93 RS features working as inversion model input for forest AGB inversion. The results showed that KNN-FIFS with the combination of Sentinel-1 and Sentinel-2 performed best at both test sites (R2 = 0.568 and RMSE = 15.05 t/hm2 for Puer and R2 = 0.511 and RMSE = 32.29 t/hm2 for Genhe). Among the three feature optimization inversion algorithms, RF performed worst for forest AGB estimation with R2 = 0.348 and RMSE = 18.06 t/hm2 for Puer and R2 = 0.345 and RMSE = 35.98 t/hm2 for Genhe using the feature combination of Sentinel-1 and Sentinel-2. The results indicated that a combination of features extracted from Sentinel-1 and Sentinel-2 can improve the inversion accuracy of forest AGB, and the KNN-FIFS algorithm has robustness and transferability in forest AGB inversions.

A machine learning-based method for multi-satellite SAR data integration

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

A FULLY CONNECTED CHANGE DETECTION METHOD OF SAR IMAGES FUSING ORIGINAL IMAGE FEATURES AND CHANGE DETECTION RESULTS

Abstract. The primary strategy to eliminate the effect of scatter noise in synthetic aperture radar (SAR) imagery is usually through filtering or combining neighborhood information. However, both approaches to reducing noise reduce the detection accuracy of change edges with similar characteristics to scatter noise points. Considering the above problems, this letter proposes a post-processing method that applies a fully connected conditional random field theoretical model to fuse the original image information with the initial change detection results. The method first takes the original image information and the initial change detection results as a priori conditions. Secondly, the global spatial information in the original image and the label values in the initial change detection results are fully considered when detecting the changed and unchanged pixels to establish a fully connected relationship between all the pixels and find the label distribution probability of each pixel under the condition of noise suppression, and finally obtain better change detection results. The experimental results on the real SAR dataset confirm the proposed method's effectiveness, robustness, and efficiency.

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

Reexamining the Estimation of Tropical Cyclone Radius of Maximum Wind from Outer Size with an Extensive Synthetic Aperture Radar Dataset

Abstract The radius of maximum wind Rmax, an important parameter in tropical cyclone (TC) ocean surface wind structure, is currently resolved by only a few sensors so that, in most cases, it is estimated subjectively or via crude statistical models. Recently, a semiempirical model relying on an outer wind radius, intensity, and latitude was fit to best-track data. In this study we revise this semiempirical model and discuss its physical basis. While intensity and latitude are taken from best-track data, Rmax observations from high-resolution (3 km) spaceborne synthetic aperture radar (SAR) and wind radii from an intercalibrated dataset of medium-resolution radiometers and scatterometers are considered to revise the model coefficients. The new version of the model is then applied to the period 2010–20 and yields Rmax reanalyses and trends that are more accurate than best-track data. SAR measurements corroborate that fundamental conservation principles constrain the radial wind structure on average, endorsing the physical basis of the model. Observations highlight that departures from the average conservation situation are mainly explained by wind profile shape variations, confirming the model’s physical basis, which further shows that radial inflow, boundary layer depth, and drag coefficient also play roles. Physical understanding will benefit from improved observations of the near-core region from accumulated SAR observations and future missions. In the meantime, the revised model offers an efficient tool to provide guidance on Rmax when a radiometer or scatterometer observation is available, for either operations or reanalysis purposes.

An SAR Imaging and Detection Model of Multiple Maritime Targets Based on the Electromagnetic Approach and the Modified CBAM-YOLOv7 Neural Network

This paper proposes an Synthetic Aperture Radar (SAR) imaging and detection model of multiple targets at the maritime scene. The sea surface sample is generated according to the composite rough surface theory. The SAR imaging model is constructed based on a hybrid EM calculation approach with the fast ray tracing strategy and the modified facet Small Slope Approximation (SSA) solution. Numerical simulations calculate the EM scattering and the SAR imaging of the multiple cone targets above the sea surface, with the scattering mechanisms analyzed and discussed. The SAR imaging datasets are then set up by the SAR image simulations. A modified YOLOv7 neural network with the Spatial Pyramid Pooling Fast Connected Spatial Pyramid Convolution (SPPFCSPC) module, Convolutional Block Attention Module (CBAM), modified Feature Pyramid Network (FPN) structure and extra detection head is developed. In the training process on our constructed SAR datasets, the precision rate, recall rate, mAP@0.5 and mAP@0.5:0.95 are 97.46%, 90.08%, 92.91% and 91.98%, respectively, after 300 rounds of training. The detection results show that the modified YOLOv7 has a good performance in selecting the targets out of the complex sea surface and multipath interference background.

Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion

Synthetic aperture radar (SAR) and optical images often present different geometric structures and texture features for the same ground object. Through the fusion of SAR and optical images, it can effectively integrate their complementary information, thus better meeting the requirements of remote sensing applications, such as target recognition, classification, and change detection, so as to realize the collaborative utilization of multi-modal images. In order to select appropriate methods to achieve high-quality fusion of SAR and optical images, this paper conducts a systematic review of current pixel-level fusion algorithms for SAR and optical image fusion. Subsequently, eleven representative fusion methods, including component substitution methods (CS), multiscale decomposition methods (MSD), and model-based methods, are chosen for a comparative analysis. In the experiment, we produce a high-resolution SAR and optical image fusion dataset (named YYX-OPT-SAR) covering three different types of scenes, including urban, suburban, and mountain. This dataset and a publicly available medium-resolution dataset are used to evaluate these fusion methods based on three different kinds of evaluation criteria: visual evaluation, objective image quality metrics, and classification accuracy. In terms of the evaluation using image quality metrics, the experimental results show that MSD methods can effectively avoid the negative effects of SAR image shadows on the corresponding area of the fusion result compared with CS methods, while model-based methods exhibit relatively poor performance. Among all of the fusion methods involved in the comparison, the non-subsampled contourlet transform method (NSCT) presents the best fusion results. In the evaluation using image classification, most experimental results show that the overall classification accuracy after fusion is better than that before fusion. This indicates that optical-SAR fusion can improve land classification, with the gradient transfer fusion method (GTF) yielding the best classification results among all of these fusion methods.

A New Strategy for Extracting 3D Deformation of Mining Areas from a Single-Geometry Synthetic Aperture Radar Dataset

This paper presents a strategy for extracting three-dimensional (3D) mining deformation from a single-geometry synthetic aperture radar (SAR) dataset. In light of the directionality of horizontal displacement caused by underground mining, we first re-model the proportional relationship between horizontal displacement and horizontal gradient of subsidence. Afterward, to improve the stability of the re-model, a solution strategy is proposed by setting different solution starting points and directions. The proposed method allows hiring of arbitrary single-geometry SAR data (e.g., air-borne, space-borne, and ground-borne SAR data) to reconstruct 3D displacements of mining areas. The proposed method has been validated through simulation and in-site data. The simulation data monitoring results indicate that the root mean square errors (RMSE) of the 3D displacements extracted by the proposed strategy are 0.45, 0.5, and 2.98 mm for the vertical subsidence, east–west, and north–south horizontal displacements, respectively. The in-site data monitoring results indicate that the RMSE of vertical subsidence compared with the leveling data are 7.3 mm. Furthermore, the MSBAS method was employed to further validate the reliability of the proposed method, the results show that the proposed method is effective to obtain the 3D deformation of the mining area, which greatly improves the applicability of SAR interferometry in the 3D deformation monitoring of the mining areas.

Automatic Matching of Optical and SAR Images Based on Attention Structure Features

Due to the disparity in imaging techniques, significant radiometric and geometric variances exist among optical and Synthetic Aperture Radar (SAR) images, making it a challenging task for achieving automatic and accurate matching in contemporary international academic research. Handcrafted structural features have shown some success in heterogeneous image matching in recent years. However, improving its matching performance manually proves to be difficult. As a result, this work presents a matching strategy based on attention-enhanced structural feature representation to improve optical and SAR images matching accuracy. In this research, a novel multi-branch global attention module is built using handmade structural feature extraction. This module can focus on the common information of structural feature descriptors in space and channel, extracting finer and more robust image features. Then, the proposed method utilizes the sum of squared difference (SSD) learning metric, which is based on the fast Fourier transform, to develop a loss function. This loss function is then used to train positive and negative samples in order to enhance the discriminative ability of the model. Experimental results obtained from training and testing on numerous optical and SAR datasets demonstrate that the proposed method significantly improves the accuracy of matching optical and SAR images compared to both current structural feature matching methods and advanced deep learning matching models.

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700.

Research on land cover classification of multi-source remote sensing data based on improved U-net network

In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent.

EddyDet: A Deep Framework for Oceanic Eddy Detection in Synthetic Aperture Radar Images

This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic eddies. We demonstrate that the EddyDet is capable of achieving acceptable eddy detection results under the condition of limited training samples, which outperforms a Mask RCNN baseline in terms of average precision. The combined Edge Head and Mask IoU Head have the ability to describe the characteristics of eddies more correctly, while the EddyDet shows great potential in practice use accurately and time efficiently, saving manual labor to a large extent.

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

SAR-optical feature matching: A large-scale patch dataset and a deep local descriptor

Image matching is a key prerequisite for image fusion. Currently, deep learning methods had shown great potential in matching. However, these methods mainly focus on optical images or area-based matching and their performance highly relies on the diversity of the training sets. This paper makes two major contributions to the synthetic aperture radar (SAR)-optical image feature matching task. First, we create a high-quality open SAR and optical patch dataset called SOPatch, which contains more than 650000 matching patch pairs. SOPatch is generated from a variety of satellite images (Sentinel-1, Sentinel-2, Gaofen-3, etc.) and contains a rich set of features (mountains, lakes, buildings, farmland, bare land, etc.), which can provide robustness to sensor and localization changes and train a model with good generalization ability. Similar to the HPatches (Balntas et al., 2017), we also add position and rotation noise to make the dataset more realistic and robust. Second, a local descriptor for SAR-optical matching called SODescNet is proposed. We first weaken the speckle noise of SAR by multiscale dilated convolution and channel attention mechanisms and then use DenseNet-CSP (cross stage partial) as the backbone for feature description. To reduce the difficulty of network training, we first use a Siamese network for pre-training and then fine-tune it via a pseudo-Siamese network. Extensive experiments show that our SODescNet is comparable or superior to state-of-the-arts.

Observing Near-Real-Time Volcanoes Deformation Using InSAR – Time Series of LiCSBAS in Indonesia

The Synthetic Aperture Radar (SAR) sensors onboard satellites are leading in advance to monitor physical changes of volcano edifices such as deformation. A few days of temporal resolution imageries provided by Sentinel-1 SAR make it possible to monitor volcanoes worldwide in near-real-time monitoring. The Sentinel-1 constellation produces a large amount of SAR images valuable for monitoring volcano deformation and hazard mitigation. However, processing the Interferometric SAR (InSAR) for a large SAR dataset is time-consuming and requires high-performance computers. Overcoming the problem, the Center for Observation & Modelling of Earthquakes, Volcanoes & Tectonics (COMET) created a program called Looking Inside the Continents from Space of Sentinel Aperture Radar (LiCSAR) integrated to InSAR time-series analysis of LiCSBAS InSAR to process and calculate the interferogram deformation in time series efficiently. This study presents the LiCSBAS observation for volcanic activities at Mts. Sinabung and Agung in North Sumatra and Bali, respectively. We have observed the activities of both volcanoes from 2015 to 2022. We have analyzed 1824 and 848 interferograms of Mt. Sinabung and Mt. Agung, respectively. The LiCSBAS InSAR time-series analysis was successfully processed for Mts. Sinabung and Agung despite being located under tropics. Velocity deformation of Mt. Sinabung is dominated by uplift around the summit, consistent with measurement by the Center for Volcanology and Geological Hazard Mitigation (CVGHM). In the period of January to March 2017, the lava dome measurement using laser distance meter showed an increase from 1.2 Mm3 to 1.7 Mm3, while LiCSBAS detected an uplift movement of 14 mm. On the contrary, the deformation of Mt. Agung at the summit of lava dome is 3 mm/yr indicate uplift, while subsidence is observed around the crater.

Estimation of lunar surface roughness using Chandrayaan-2 full-polarimetric DFSAR data

Polarimetric radar backscattering coefficients depend on the impinging frequency, dielectric constant, incidence angle, polarization, and surface roughness. Therefore, surface roughness can be estimated using high-resolution polarimetric radar datasets. For this purpose, we examine the utility of model-based and eigenvalue-based decomposition approaches in this work. While using model-based decomposition, the dielectric constant is estimated at the outset. Thereafter, the rms height is derived from a scalar multiplier that models surface roughness. We also propose a modified single-bounce eigenvalue relative difference (SERD) and establish that it is a better indicator of surface roughness than the circular polarization coherence and the original SERD for non-reflection-symmetric lunar terrain. The Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) datasets acquired over three Apollo mission landing sites are used for demonstration.

Revealing the Land Subsidence Deceleration in Beijing (China) by Gaofen-3 Time Series Interferometry

Revealing the land subsidence in Beijing, China, induced by the massive groundwater extraction in the past three decades, is important to mitigate the hazards and protect the residences and infrastructure. Many SAR (Synthetic Aperture Radar) datasets have been successfully applied to reveal the land subsidence over Beijing in previous research, while few works were achieved on land subsidence revealed by time-series InSAR (Interferometric Synthetic Aperture Radar) with Gaofen-3 SAR images. In this study, we successfully perform the time-series InSAR analysis with Gaofen-3 SAR images to extract the land subsidence in Beijing from 2020 to 2021. The Sentinel-1 SAR images were used to assess the accuracy of Gaofen-3 images. The subsidence scale and extent are consistent in detected major subsidence bowls between the two datasets. The spatial–temporal evolution and the deceleration of Beijing land subsidence were revealed by comparing with the Sentinel-1 results from 2017 to 2020. Moreover, we evaluated the interferometric performance of Gaofen-3 satellite SAR imagery and analyzed the main factors that mostly influence the coherence and quality of interferograms. Our results proved that the long perpendicular baselines decrease the coherence seriously over the study area, and the artifacts induced by inaccurate orbit information reduce the quality of the Gaofen-3 interferograms. Refining and removing the two main artifacts could improve the quality of interferograms formed by Gaofen-3 SAR images.

Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic aperture radar (SAR) renders the identification of false alarms using detectors challenging. Polarimetric SAR has the potential to improve ship detection performance owing to its distinctive polarization characteristics. The present study proposes a dualistic cascade convolutional neural network (DCCNN) algorithm driven by polarization characteristics for ship detection with fully PolSAR data. First, the new characterizations of fully PolSAR data—Optimized SPAN (OSPAN) and 6-D polarization vector (P6), were mined and defined based on the polarization coherence matrix to introduce more intact information of targets. Then, a backbone feature extraction network with parallel dualistic cascade architecture, basic geometric feature extraction network (BGFENet), and polarization feature enhancement network (PFENet) was specifically constructed, which provided the comprehensive feature representation of targets via feature fusion. Finally, the classification and regression tasks were accomplished in the fully convolutional detection subnetwork relying on extracted multi-scale fusion feature maps, while focusing on target location regression, leading to a decrease in the cost of detection efficiency caused by redundant PFENet. In addition, the corresponding training strategy according to the special architecture of DCCNN was designed to overcome the problem of labeled fully PolSAR data insufficiency and migrate refined geometric knowledge of targets in the single-polarization SAR amplitude image. Experimental results on the established fully polarized SAR dataset show that DCCNN outperforms the competitive CNN-based target detection methods by at least 6.79% in terms of average precision. Moreover, experimental results on the typical large scenes show that DCCNN is at least 0.88% higher than the well-known conventional methods in terms of F1 score.

Utilising Sentinel-1's Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter.

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite's high orbital stability. By propagating Sentinel-1's orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10-50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.