Information Of Synthetic Aperture Radar Images Research Articles

Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery

This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of SAR images to improve the model's classification capability. The model is subsequently fine-tuned using lead samples to identify leads. 24 Sentinel-1 SAR images acquired in the Beaufort Sea are utilized for model training and testing. The accuracy (Acc), mean intersection over union (mIoU), and kappa coefficient (Kappa) are employed as evaluation metrics. Experiments show that DBU-Net achieves 91.83%/0.841/0.849 in Acc/mIoU/Kappa in classifying MYI, FYI, and OW, significantly outperforming three traditional models based on support vector machine, random forest, or convolutional neural network. Compared with the original U-Net, the dual-branch encoder and the GLCMs improve 1.45%/4.4%/2.8% in Acc/mIoU/Kappa in MYI, FYI, and OW. Acc/mIoU/Kappa metrics of leads detection is 99.49%/0.801/0.754. Besides, 454 Sentinel-1 SAR images are fed into the optimal DBU-Net to generate 80 m sea ice products in the Beaufort Sea for winters 2018–2022. As the MYI draws wide attention and the FYI and MYI are complementary in the area during the Winter, we discuss the variation of MYI based on the generated sea ice products and explore the relationship between MYI's variation and the Beaufort High. We found that the MYI export in the 2018/19 winter was due to large summer sea ice remains and the abnormal sea ice motion caused by the southeast shifting Beaufort Atmospheric Pressure High (Beaufort High). The MYI import in the 2020/21 winter was due to a strong northward MYI import caused by the powerful Beaufort High.

Wide-Area Subsidence Monitoring and Analysis Using Time-Series InSAR Technology: A Case Study of the Turpan Basin

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

Synthetic aperture radar (SAR) images are inevitably interspersed with speckle noise due to their coherent imaging mechanism, which greatly hinders subsequent related research and application. In recent studies, deep learning has become an effective tool for despeckling remote sensing images. However, preserving more texture details while removing speckle noise remains a challenging task in the field of SAR image despeckling. Furthermore, most despeckling algorithms are designed specifically for a specific look and seriously lack generalizability. Therefore, in order to remove speckle noise in SAR images, a novel end-to-end frequency domain decomposition network (SAR−FDD) is proposed. The method first performs frequency domain decomposition to generate high-frequency and low-frequency information. In the high-frequency branch, a mean filter is employed to effectively remove noise. Then, an interactive dual-branch framework is utilized to learn the details and structural information of SAR images, effectively reducing speckles by fully utilizing features from different frequencies. In addition, a blind denoising model is trained to handle noisy SAR images with unknown noise levels. The experimental results demonstrate that the SAR−FDD achieves good visual effects and high objective evaluation metrics on both simulated and real SAR test sets (peak signal-to-noise ratio (PSNR): 27.59 ± 1.57 and structural similarity index (SSIM): 0.78 ± 0.05 for different speckle noise levels), demonstrating its strong denoising performance and ability to preserve edge textures.

Non-Local SAR Image Despeckling Based on Sparse Representation

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

Target Recognition in SAR Images Using Complex-Valued Network Guided with Sub-Aperture Decomposition

Synthetic aperture radar (SAR) images have special physical scattering characteristics owing to their unique imaging mechanism. Traditional deep learning algorithms usually extract features from real-valued SAR images in a purely data-driven manner, which may ignore some important physical scattering characteristics and sacrifice some useful target information in SAR images. This undoubtedly limits the improvement in performance for SAR target recognition. To take full advantage of the physical information contained in SAR images, a complex-valued network guided with sub-aperture decomposition (CGS-Net) for SAR target recognition is proposed. According to the fact that different targets have different physical scattering characteristics at different angles, the sub-aperture decomposition is used to improve accuracy with a multi-task learning strategy. Specifically, the proposed method includes main and auxiliary tasks, which can improve the performance of the main task by learning and sharing useful information from the auxiliary task. Here, the main task is the target recognition task, and the auxiliary task is the target reconstruction task. In addition, a complex-valued network is used to extract the features from the original complex-valued SAR images, which effectively utilizes the amplitude and phase information in SAR images. The experimental results obtained using the MSTAR dataset illustrate that the proposed CGS-Net achieved an accuracy of 99.59% (without transfer learning or data augmentation) for the ten-classes targets, which is superior to the other popular deep learning methods. Moreover, the proposed method has a lightweight network structure, which is suitable for SAR target recognition tasks because SAR images usually lack a large number of labeled data. Here, the experimental results obtained using the small dataset further demonstrate the excellent performance of the proposed CGS-Net.

Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q, which is used to generate three vegetation descriptors: the co-pol purity parameter (mcp), the pseudo-scattering angle (θcp), and the pseudo-scattering entropy (Hcp). Further, a novel unsupervised clustering framework, founded on Hcp and θcp, has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that mcp,θcp, and Hcp exhibit regular changes at different growth stages of crops from planting to maturity, with mcp and θcp gradually decreasing while Hcp gradually increasing. Within the entire phenology window, θcp changes by approximately 42°, while both mcp and θcp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources.

SAR Image Reconstruction and Autofocus Using Complex-Valued Feature Prior and Deep Network Implementation

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing electromagnetic images of the observation scene. The prior knowledge-based SAR image reconstruction method can reduce the requirement of data sampling ratio and improve image quality. The existing prior knowledge-based method usually uses the magnitude information of SAR images while ignoring the phase information. However, since the echo and backscattering coefficients are complex values, the phase information of SAR images will help improve the reconstruction accuracy in the image reconstruction process. To improve the reconstruction performance, this paper proposes a SAR image reconstruction and autofocus method using complex-valued feature prior. In the proposed method, the complex-valued feature prior is learned from data by a complex-valued feature projection operator (CFPO), which can characterize and extract scene features in Range-Doppler domain and 2D frequency domain. The proposed CFPO enables more efficient use of echo data and helps to improve image reconstruction and autofocus performance. In addition, the proposed method is implemented by an unfolded deep network, which enables data-driven feature learning and efficient computation. The proposed method is verified by simulated and measured data.

Frequency-Adaptive Learning for SAR Ship Detection in Clutter Scenes

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed Frequency Attention Module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the High-Resolution SAR images dataset (HRSID) and SAR Ship Detection Dataset (SSDD). Compared with the baseline method YOLOv5 and existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both datasets.

HA-Unet: A Modified Unet Based on Hybrid Attention for Urban Water Extraction in SAR Images

Urban water plays a significant role in the urban ecosystem, but urban water extraction is still a challenging task in automatic interpretation of synthetic aperture radar (SAR) images. The influence of radar shadows and strong scatters in urban areas may lead to misclassification in urban water extraction. Nevertheless, the local features captured by convolutional layers in Convolutional Neural Networks (CNNs) are generally redundant and cannot make effective use of global information to guide the prediction of water pixels. To effectively emphasize the identifiable water characteristics and fully exploit the global information of SAR images, a modified Unet based on hybrid attention mechanism is proposed to improve the performance of urban water extraction in this paper. Considering the feature extraction ability and the global modeling capability in SAR image segmentation, the Channel and Spatial Attention Module (CSAM) and the Multi-head Self-Attention Block (MSAB) are both introduced into the proposed Hybrid Attention Unet (HA-Unet). In this work, Resnet50 is adopted as the backbone of HA-Unet to extract multi-level features of SAR images. During the feature extraction process, CSAM based on local attention is adopted to enhance the meaningful water features and ignore unnecessary features adaptively in feature maps of two shallow layers. In the last two layers of the backbone, MSAB is introduced to capture the global information of SAR images to generate global attention. In addition, two global attention maps generated by MSAB are aggregated together to reconstruct the spatial feature relationship of SAR images from high-resolution feature maps. The experimental results on Sentinel-1A SAR images show that the proposed urban water extraction method has a strong ability to extract water bodies in the complex urban areas. The ablation experiment and visualization results vividly indicate that both CSAM and MSAB contribute significantly to extracting urban water accurately and effectively.

PCBA-Net: Pyramidal Convolutional Block Attention Network for Synthetic Aperture Radar Image Change Detection

Synthetic aperture radar (SAR) imagery change detection (CD) is still a crucial and challenging task. Recently, with the boom of deep learning technologies, many deep learning methods have been presented for SAR CD, and they achieve superior performance to traditional methods. However, most of the available convolutional neural networks (CNN) approaches use diminutive and single convolution kernel, which has a small receptive field and cannot make full use of the context information and some useful detail information of SAR images. In order to address the above drawback, pyramidal convolutional block attention network (PCBA-Net) is proposed for SAR image CD in this study. The proposed PCBA-Net consists of pyramidal convolution (PyConv) and convolutional block attention module (CBAM). PyConv can not only extend the receptive field of input to capture enough context information, but also handles input with incremental kernel sizes in parallel to obtain multi-scale detailed information. Additionally, CBAM is introduced in the PCBA-Net to emphasize crucial information. To verify the performance of our proposed method, six actual SAR datasets are used in the experiments. The results of six real SAR datasets reveal that the performance of our approach outperforms several state-of-the-art methods.

Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model

The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

Road Network Extraction from SAR Images with the Support of Angular Texture Signature and POIs

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China’s Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information.

Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion

Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images.

SAR Image Fusion Classification Based on the Decision-Level Combination of Multi-Band Information

Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach.

Unsupervised Deep Sparse Features Extraction for SAR Image Segmentation

Deep learning (DL) methods usually need to collect a large amount of labeled data to extract deep features. However, due to the difficulty of obtaining numerous labeled data from synthetic aperture radar (SAR) images, unsupervised feature learning has been focused on SAR image processing. In this paper, we propose a three-dimensional sparse model (3-DSM) to extract deep sparse features from SAR images in an unsupervised way. Concretely, 3-DSM learns the convolution kernels by minimizing the error between the features and the constructed sparse maps, without labeled samples. Thus, the discriminative features can be extracted in an unsupervised way by the learned convolution kernels and are able to capture the main structure information of SAR images. Furthermore, to the best of our knowledge, 3-DSM firstly specifies the sparsity of convolution kernels, with each convolution kernel exhibiting its independence from the others and the redundancy of convolution kernels being diminishing. It means that each convolution kernel extracts its unique structural features of SAR images. Consequently, in the feature extraction, three-dimensional sparsities have been specified, including width, height, and depth, with the acquisition of discriminative less-redundant features. The effectiveness of 3-DSM is demonstrated by the feature extraction and segmentation of the simulated and real SAR images.

A Hybrid Network With Structural Constraints for SAR Image Scene Classification

Data-based image classification methods, such as convolutional neural networks (CNNs), have achieved state-of-the-art performance. They usually leverage thousands of labeled samples to train the networks but ignore some prior knowledge. However, labeled samples are difficult to be obtained for synthetic aperture radar (SAR) images. Model-based methods are adept at utilizing the prior information of data, while they have to introduce some restrictions or assumptions during the realization of models. Consequently, to develop the advantages of both methods and improve their disadvantages, we propose a hybrid network by coupling the data-based with model-based methods for SAR image scene classification in this article. First, to fully use the prior information of SAR images and large amounts of unlabeled samples, we improve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{0}$ </tex-math></inline-formula> -based variational Bayesian inference model (GVBI) and construct a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G^{0}$ </tex-math></inline-formula> -based convolutional variational auto-encoder (GCVAE) for unsupervised learning of the distributional characteristics of SAR images. After that, we further extend the GCVAE by combining it with CNN, resulting in a stronger hybrid network to classify SAR images with a few labeled samples. In addition, considering the abundant structural information is crucial for SAR image classification, we design a sketch fitter and two structural constraints on both pixel and sketch spaces to assist the hybrid network to improve its classification performance. Finally, we evaluate the performance of our method on real-SAR images, and the experimental results demonstrate that the proposed framework outperforms related methods on classification while reducing the manual annotation substantially.

Target Detection Network for SAR Images Based on Semi-Supervised Learning and Attention Mechanism

The existing Synthetic Aperture Radar (SAR) image target detection methods based on convolutional neural networks (CNNs) have achieved remarkable performance, but these methods require a large number of target-level labeled training samples to train the network. Moreover, some clutter is very similar to targets in SAR images with complex scenes, making the target detection task very difficult. Therefore, a SAR target detection network based on a semi-supervised learning and attention mechanism is proposed in this paper. Since the image-level label simply marks whether the image contains the target of interest or not, which is easier to be labeled than the target-level label, the proposed method uses a small number of target-level labeled training samples and a large number of image-level labeled training samples to train the network with a semi-supervised learning algorithm. The proposed network consists of a detection branch and a scene recognition branch with a feature extraction module and an attention module shared between these two branches. The feature extraction module can extract the deep features of the input SAR images, and the attention module can guide the network to focus on the target of interest while suppressing the clutter. During the semi-supervised learning process, the target-level labeled training samples will pass through the detection branch, while the image-level labeled training samples will pass through the scene recognition branch. During the test process, considering the help of global scene information in SAR images for detection, a novel coarse-to-fine detection procedure is proposed. After the coarse scene recognition determining whether the input SAR image contains the target of interest or not, the fine target detection is performed on the image that may contain the target. The experimental results based on the measured SAR dataset demonstrate that the proposed method can achieve better performance than the existing methods.

SAR Image Recording Strategy to Improve Characteristics

The low information consistency and serious interference caused by synthetic aperture radar (SAR) imaging characteristics and target attributes affect the accuracy of SAR image information description, which reduces the performance of SAR image registration. In this work, the polar speeded up robust features (PSURF) descriptor is proposed for SAR image registration. In PSURF, according to the advantages of pole space in the relationship between pixels, a quantitative model of information change based on pole space is established to improve the integrity, consistency, and separability of image features. Firstly, the support region is transformed into polar coordinates. Then, the image gray level changes in the radial and angular directions are quantified with different scales. Finally, the PSURF descriptor based on the scale of keypoints is constructed covering the omnidirectional space.

Airport Detection in SAR Images Via Salient Line Segment Detector and Edge-Oriented Region Growing

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections. In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images. First, a salient line segment detector is constructed to extract salient line segments in the SAR images. Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features. Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images. Using EORG, the airport region can be mapped by performing region growing with these seeds. We implement experiments on real radar images to validate the effectiveness of our method. The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms.

Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way

Synthetic aperture radar (SAR) tomography (TomoSAR) technique can eliminate severe overlap in 2D images, and improve target recognition and 3D modeling capabilities, which has become an important trend in SAR development. In recent years, rapid progress has been made and various algorithms were proposed. The Aerospace Information Research Institute, Chinese Academy of Sciences produced the first airborne array TomoSAR system in China. In this paper an airborne TomoSAR experiment using this system and the overall processing flow considering the characteristics of this system are introduced. So far, most of the 3D imaging algorithms used in TomoSAR processing are conducted pixel by pixel, which ignore the structural connections among adjacent pixels. The results will be corrupted by outliers and artifacts due to noise or other uncertainties during processing. To solve this problem, the contextual information of SAR images are explored and utilized by means of the Local Gaussian Markov Random Field (LGMRF) in our 3D reconstruction method. Comparisons with traditional methods are presented to verify the effectiveness of proposed method. The overall processing methods are applied to the data acquired in this experiment and a large scale urban 3D point cloud is obtained, which verifies the ability of the entire system in 3D imaging. This paper presents both the algorithms and experimental results of airborne TomoSAR system, which will contribute in several ways to our understanding of SAR 3D imaging and provide a basis for further research.