Synthetic Aperture Radar Segmentation Research Articles

Near Real-Time Mapping of Tropical Forest Disturbance Using SAR and Semantic Segmentation in Google Earth Engine

Satellite-based forest alert systems are an important tool for ecosystem monitoring, planning conservation, and increasing public awareness of forest cover change. Continuous monitoring in tropical regions, such as those experiencing pronounced monsoon seasons, can be complicated by spatially extensive and persistent cloud cover. One solution is to use Synthetic Aperture Radar (SAR) imagery acquired by the European Space Agency’s Sentinel-1A and B satellites. The Sentinel 1A and B satellites acquire C-band radar data that penetrates cloud cover and can be acquired during the day or night. One challenge associated with operational use of radar imagery is that the speckle associated with the backscatter values can complicate traditional pixel-based analysis approaches. A potential solution is to use deep learning semantic segmentation models that can capture predictive features that are more robust to pixel-level noise. In this analysis, we present a prototype SAR-based forest alert system that utilizes deep learning classifiers, deployed using the Google Earth Engine cloud computing platform, to identify forest cover change with near real-time classification over two Cambodian wildlife sanctuaries. By leveraging a pre-existing forest cover change dataset derived from multispectral Landsat imagery, we present a method for efficiently developing a SAR-based semantic segmentation dataset. In practice, the proposed framework achieved good performance comparable to an existing forest alert system while offering more flexibility and ease of development from an operational standpoint.

A Novel Unsupervised Evaluation Metric Based on Heterogeneity Features for SAR Image Segmentation

The segmentation of Synthetic aperture radar (SAR) images is vital and fundamental in SAR image processing, so evaluating segmentation results without ground truth (GT) is an essential part in segmentation algorithms comparison, parameters selection, and optimization. In this study, we first extracted the heterogeneous features (HF) of SAR images to adequately describe the SAR image targets, which were extracted by the proposed intensity feature extractor (IFEE) based on edge-hold and two fruitful methods. Then we proposed a novel and effective unsupervised evaluation (UE) metric <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> to evaluate the SAR image segmentation results, which metric based on HF using the global intra-segment homogeneity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GHO</i> ), global inter-segment heterogeneity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GHE</i> ), and edge validity index ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVI</i> ) as local segmentation measures. The effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GHO</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GHE</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EVI,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> was revealed by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. In experiments, four segmentation algorithms are used to segment plenty of synthetic and real SAR images as the evaluation objects, and four widely used metrics are utilized for comparison. The results show the effectiveness, and superiority of the proposed metric. Moreover, the mean correlation between the proposed UE metric and the SE metric is more than 0.67 and 0.99, which indicates that the proposed metric helps in choosing parameters of segmentation algorithms without GT.

E-MPSPNet: Ice–Water SAR Scene Segmentation Based on Multi-Scale Semantic Features and Edge Supervision

Distinguishing sea ice and water is crucial for safe navigation and carrying out offshore activities in ice zones. However, due to the complexity and dynamics of the ice–water boundary, it is difficult for many deep learning-based segmentation algorithms to achieve accurate ice–water segmentation in synthetic aperture radar (SAR) images. In this paper, we propose an ice–water SAR segmentation network, E-MPSPNet, which can provide effective ice–water segmentation by fusing semantic features and edge information. The E-MPSPNet introduces a multi-scale attention mechanism to better fuse the ice–water semantic features and designs an edge supervision module (ESM) to learn ice–water edge features. The ESM not only provides ice–water edge prediction but also imposes constraints on the semantic feature extraction to better express the edge information. We also design a loss function that focuses on both ice–water edges and semantic segmentations of ice and water for overall network optimization. With the AI4Arctic/ASIP Sea Ice Dataset as the benchmark, experimental results show our E-MPSPNet achieves the best performance compared with other commonly used segmentation models, reaching 94.2% for accuracy, 93.0% for F-score, and 89.2% for MIoU. Moreover, our E-MPSPNet shows a relatively smaller model size and faster processing speed. The application of the E-MPSPNet for processing a SAR scene demonstrates its potential for operational use in drawing near real-time navigation charts of sea ice.

SAR Image Segmentation by Efficient Fuzzy C-Means Framework with Adaptive Generalized Likelihood Ratio Nonlocal Spatial Information Embedded

The existence of multiplicative noise in synthetic aperture radar (SAR) images makes SAR segmentation by fuzzy c-means (FCM) a challenging task. To cope with speckle noise, we first propose an unsupervised FCM with embedding log-transformed Bayesian non-local spatial information (LBNL_FCM). This non-local information is measured by a modified Bayesian similarity metric which is derived by applying the log-transformed SAR distribution to Bayesian theory. After, we construct the similarity metric of patches as the continued product of corresponding pixel similarity measured by generalized likelihood ratio (GLR) to avoid the undesirable characteristics of log-transformed Bayesian similarity metric. An alternative unsupervised FCM framework named GLR_FCM is then proposed. In both frameworks, an adaptive factor based on the local intensity entropy is employed to balance the original and non-local spatial information. Additionally, the membership degree smoothing and the majority voting idea are integrated as supplementary local information to optimize segmentation. Concerning experiments on simulated SAR images, both frameworks can achieve segmentation accuracy of over 97%. On real SAR images, both unsupervised FCM segmentation frameworks work well on SAR homogeneous segmentation in terms of region consistency and edge preservation.

SAR image segmentation with combined Bayes’ rule, GMTRJ, and EM algorithms

ABSTRACT This study analyses the roles of all the classes in the segmentation procedure, and aims to improve the segmentation accuracy of synthetic aperture radar (SAR) images. Accordingly, weight variables of all classes for SAR images are defined and incorporated into the segmentation model by Bayes’ rule and geometric partition tessellation. In addition, the Generalized Multiple-Try Reversible Jump (GMTRJ) and expectation maximization (EM) algorithms are designed to obtain the values of weight variables and the optimal segmentation by simulating the segmentation model. The greater the value of the weight variable, the more important the role of its class. The proposed approach can automatically determine the classes’ roles in the segmentation procedure and segment SAR images accurately and quickly. Finally, the proposed and comparison approaches are tested on SAR images, and the results fully demonstrate the effectiveness of the proposed approach.

SAR Image Segmentation Based on Fisher Vector Superpixel Generation and Label Revision

This paper addresses the problem of superpixel-bases segmentation of synthetic aperture radar (SAR) images. Most superpixel segmentation methods have difficulties in segmenting adjacent regions with similar gray values, due to only considering spatial and gray information. To solve this problem and improve segmentation accuracy, this paper proposes a SAR image segmentation method based on Fisher vector superpixel generation and label revision. Firstly, the Fisher vector is obtained by processing the Gaussian mixture function. By introducing the Fisher vector, a distance formula is constructed for superpixel segmentation. Therefore, the adjacent regions with similar gray values can be segmented effectively in the generated superpixel map. Secondly, the superpixels are clustered using the K-means algorithm to obtain the initial label map. Then, with extracted edge information as constraints, the pixel labels obtained by K-means are repaired pixel by pixel to get the middle label map, according to the number and gray value difference of labels. This overcomes influence of noise generated by K-means. Finally, the middle label map is relabeled using the region growth algorithm to divide pixel blocks. Isolated pixel blocks surrounded by similar labels are corrected, based on the gray mean difference. The final label result has a better segmentation accuracy. Experiments on synthetic SAR images and real images demonstrate that the proposed algorithm achieves higher segmentation accuracy than six state-of-the-art clustering algorithms for SAR image segmentation.

Target Region Segmentation in SAR Vehicle Chip Image With ACM Net

Target region segmentation of synthetic aperture radar (SAR) images is one of the challenging problems in SAR image interpretation. The existing conventional segmentation methods rely on parameter selection in different backgrounds. Compared with traditional methods, the deep-learning-based methods can reduce the dependency on parameters and achieve more accurate results. However, lacking annotation data limits the application of the deep-learning-based methods in SAR chip image segmentation aspect. To solve these problems, a refined network structure for SAR vehicle image semantic segmentation, namely, All-Convolutional networks (A-ConvNets)-based Mask (ACM) net, is proposed. The mask in the training dataset of the network is extracted from image reconstruction using the Attribute Scattering Center (ASC) model, which can solve the problem of the lack of manual annotation in the segmentation methods based on deep learning. The proposed ACM Net consists of a modified A-ConvNets-based backbone and two decoupled head branches which achieve target segmentation and label prediction results, respectively. Experiments on moving and stationary target acquisition and recognition (MSTAR) dataset show that the comprehensive segmentation performance of ACM Net is better than both traditional segmentation methods and deep-learning-based segmentation methods. The classification results outperform other instance or semantic segmentation methods with the state-of-the-art recognition accuracy.

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized, and the sea&#x2013;land segmentation process to remove land false alarms is time-consuming. Regarding these problems, first, a single low-level path aggregation network is designed specifically for small targets. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Second, adaptive dual-polarimetric feature fusion is proposed to filter the multichannel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Third, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset containing small vessel detection and sea&#x2013;land segmentation labels is created with reference to the LS-SSDDv1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

Water body segmentation of Synthetic Aperture Radar image using Deep Convolutional Neural Networks

Deep Convolutional Neural Networks are finding their way into modern machine learning tasks and proved themselves to become one of the best contenders for future development in the field. Several proposed methods in image segmentation and classification problems are giving us satisfactory results and could even perform better than humans in image recognition tasks. But also at the cost of their performance, they also require a huge amount of images for training and huge amount of computing power and time that makes them unrealistic in some situations where obtaining a large dataset is not feasible. In this work, an attempt is made for segmentation of Synthetic Aperture Radar (SAR) images which are not usually abundant enough for training, and are heavily affected by a kind of multiplicative noise called speckle noise. For the segmentation task, pre-defined filters are first applied to the images and are fed to hybrid CNN that is resulted from the concept of Inception and U-Net. The outcome of our proposed method has been examined for their effectiveness of application in a complete set of SAR images that are not used for training. The accuracy has also been compared with the manually annotated SAR images.

Semantic segmentation of PolSAR image data using advanced deep learning model

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

Hierarchical fusion convolutional neural networks for SAR image segmentation

Abstract Convolutional neural network (CNN) has achieved promising results in image segmentation recently. However, for the segmentation of synthetic aperture radar (SAR) images with complicated scene, the single receptive field in CNN has a limited ability to effectively capture structural and regional information at the same time. In this paper, we propose a hierarchical fusion CNN (HIFCNN) model for SAR image segmentation. At each convolutional layer, HIFCNN sets several different-sized receptive fields, and thus extracts hierarchical features. Concretely, the larger-sized receptive field captures regional information and is robust against speckle, while the smaller one preserves the structural information well. Then, based on the Dempster-Shafer evidential theory, the proposed hierarchical network, HIFCNN, implements a decision-level fusion to integrate these hierarchical features. In this way, the structural and regional information can be accurately captured by different receptive fields, which is beneficial for edge location, structure preservation and region homogeneity in SAR image segmentation. The effectiveness of HIFCNN model is demonstrated by the application to the segmentation of the simulated images and real SAR images.

CVCMFF Net: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas.

A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution

Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other applications, is one of the most difficult challenges. This paper proposes an encoding-decoding network based on Deeplabv3+ to semantically segment SAR imagery. A new potential energy loss function based on the Gibbs distribution is proposed here to establish the semantic dependence among different categories through the relationship among different cliques in the neighborhood system. This paper introduces an improved channel and spatial attention module to the Mobilenetv2 backbone to improve the recognition accuracy of small object categories in SAR imagery. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.

Fine Building Segmentation in High-Resolution SAR Images Via Selective Pyramid Dilated Network

The building extraction from synthetic aperture radar (SAR) images has always been a challenging research topic. Recently, the deep convolution neural network brings excellent improvements in SAR segmentation. The fully convolutional network and other variants are widely transferred to the SAR studies because of their high precision in optical images. They are still limited by their processing in terms of the geometric distortion of buildings, the variability of building structures, and scattering interference between adjacent targets in the SAR images. In this article, a unified framework called selective spatial pyramid dilated (SSPD) network is proposed for the fine building segmentation in SAR images. First, we propose a novel encoder–decoder structure for the fine building feature reconstruction. The enhanced encoder and the dual-stage decoder, composed of the CBM and the SSPD module, extract and recover the crucial multiscale information better. Second, we design the multilayer SSPD module based on the selective spatial attention. The multiscale building information with different attention on multiple branches is combined, optimized, and adaptively selected for adaptive filtering and extracting features of complex multiscale building targets in SAR images. Third, according to the building features and SAR imaging mechanism, a new loss function called L-shape weighting loss (LWloss) is proposed to heighten the attention on the L-shape footprint characteristics of the buildings and reduce the missing detection of line buildings. Besides, LWloss can also alleviate the class imbalance problem in the optimization stage. Finally, the experiments on a large-scene SAR image dataset demonstrate the effectiveness of the proposed method and verify its superiority over other approaches, such as the region-based Markov random field, U-net, and DeepLabv3+.

Fast Multiscale Superpixel Segmentation for SAR Imagery

Superpixel segmentation is essential to the rapid information extraction from synthetic aperture radar (SAR) imagery. In this letter, we propose a fast multiscale superpixel segmentation method based on the minimum spanning tree (MST), which can generate all scales of superpixels accurately in real time. Therefore, our method has the ability to segment SAR imagery with different scales efficiently and is meaningful for applications that require different levels of SAR image details. Experimental results on two real SAR images demonstrate that our proposed superpixel segmentation method can capture the image information of different levels, resulting in better hierarchical segmentation performance in comparison with other state-of-the-art methods.

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

AN ENERGY SEGMENTATION METHOD OF HIGH-RESOLUTION SAR IMAGE BASED ON MULTIPLE FEATURES

Abstract. To achieve the optimal image segmentation, an energy segmentation method based on multiple features and blocks for high resolution Synthetic Aperture Radar (SAR) image is proposed in this paper. First of all, a feature vector of pixel is formed with the texture feature extracted by curvelet transform and means function, the boundary feature extracted by curvelet transform and Canny Operator, and the original spectral feature; a feature set is formed by all feature vectors of pixels in the image. The feature vector is considered as segmentation basis, and its domain is partitioned by regular tessellation. On the partitioned image domain, a label variable is assigned to a regular block; each homogeneous region is fitted by one or more regular blocks; Obviously, a label field is formed by all the label variables of regular blocks. The model of label field is built by using energy function of neighborhood relationship. The feature set is considered as a realization of a random filed of multiple features (multiple features field for short). A heterogeneous energy function is used to establish the model of multiple features field. Then the established models of the label field and multiple features field are combined to define global energy function of image segmentation, and non-constrained Gibbs probability distribution is used to describe the global energy function to build the energy segmentation model based on multiple features. Further, a RJMCMC algorithm is designed to simulate from the model to segment SAR image. To verify the feasibility and superiority of the proposed approach, real SAR images are tested.

A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation

This paper proposes a novel algorithm for segmentation of synthetic aperture radar (SAR) image, our proposed algorithm (THFCM) is based on thumbnail representations and a hierarchical fuzzy C-means (FCM) approach. THFCM firstly divides the image into pixel groups to extract local feature, and the major pixels of each pixel group are selected to construct a thumbnail. FCM is then used to segment each thumbnail, and hierarchical segmentation is then performed on the overall image data, based on the results of thumbnail clustering. The thumbnail approach leverages local image information, helping to overcome speckle noise, while the hierarchical approach improves computational efficiency. Experiments on simulated and real SAR images suggest that THFCM outperforms several other state-of-the-art algorithms in terms of both segmentation accuracy and running time.

Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation

Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.

Superpixel-Based LCM Detector for Faint Ships Hidden in Strong Noise Background SAR Imagery

Faint or small target detection is always a challenging task for image-based remote sensing application. Due to low signal-to-clutter ratio, many conventional methods fail to yield satisfactory results. To solve the problem, a superpixel-based local contrast measure (SLCM) detector is proposed against the faint ship targets hidden in strong noise background synthetic aperture radar (SAR) images. The detection stage consists of superpixel segmentation and SLCM detection. In the first stage, a modified simple linear iterative clustering algorithm is utilized to segment SAR image into superpixels. In the second stage, a novel SLCM is performed on the generated superpixels to enhance and find the hidden ships. Experiments on real SAR images indicate that the proposed method can effectively detect ship targets from the strong noise background SAR images.