Original Synthetic Aperture Radar Image Research Articles

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.

Water-Body Detection in Sentinel-1 SAR Images with DK-CO Network

Synthetic Aperture Radar (SAR) is an active microwave sensor with all-day/night and all-weather detection capability, which is crucial for detecting surface water resources. Surface water-body such as rivers, lakes, reservoirs, and ponds usually appear as dark areas in SAR images. Accurate and automated extraction of these water bodies can provide valuable data for the management and strategic planning of surface water resources and effectively help prevent and control drought and flood disasters. However, most deep learning-based methods rely on manually labeled samples for model training and testing, which is inefficient and may introduce errors. To address this problem, this paper proposes a novel water-body detection method that combines optimization algorithms and deep learning techniques to automate water-body label extraction and improve the accuracy of water-body detection. First, this paper uses a swarm intelligence optimization algorithm, Dung Beetle Optimizer (DBO), to optimize the initial cluster center of the K-means clustering algorithm, which is called the DBO-K-means (DK) method. The DK method divides the training images into four categories and extracts the water bodies in them to generate the water-body labels required for deep learning model training and testing, and the whole process does not require human intervention. Then, the labels generated by DK and training data set images are fed into the Classifier–Optimizer (CO) for training. The classifier performs a dense classification task at the pixel level, resulting in an initial result image with blurred boundaries of the water body. Then, the optimizer takes this preliminary result image and the original SAR image as input, performs fine-grained optimization on the preliminary result, and finally generates a result image with a clear water-body boundary. Finally, we evaluated the accuracy of water-body detection using multiple performance indicators including ACC, precision, F1-Score, recall, and Kappa coefficient. The results show that the values of all indicators exceed 93%, which demonstrates the high accuracy and reliability of our proposed water-body detection method. Overall, this paper presents a novel DK-based approach that improves the automation and accuracy of deep learning methods for detecting water bodies in SAR images by enabling automatic sample extraction and optimization of deep learning models.

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

One of the major threats to marine ecosystems is pollution, particularly, that associated with the offshore oil and gas industry. Oil spills occur in the world's oceans every day, either as large-scale spews from drilling-rig or tanker accidents, or as smaller discharges from all sorts of sea-going vessels. In order to contribute to the timely detection and monitoring of oil spills over the oceans, we propose a new Multi-channel Deep Neural Network (M-DNN) segmentation model and a new and effective Synthetic Aperture Radar (SAR) image dataset, that enable us to emit forewarnings in a prompt and reliable manner. Our proposed M-DNN is a pixel-level segmentation model intended to improve previous DNN oil-spill detection models, by taking into account multiple input channels, complex oil shapes at different scales (dimensions) and evolution in time, and look-alikes from low wind speed conditions. Our methodology consists of the following components: 1) New Multi-channel SAR Image Database Development; 2) Multi-Channel DNN Model based on U-net and ResNet; and 3) Multi-channel DNN Training and Transfer Learning. Due to the lack of public oil spill databases guaranteeing a correct learning process of the M-DNN, we developed our own database consisting of 16 ENVISAT-ASAR images acquired over the Gulf of Mexico during the Deepwater Horizon (DWH) blowout, off the west coast of South Korea during the Hebei Spirit oil tanker collision, and over the Black Sea. These images were pre-processed to create a 3-channel input image IM = {IO, IW, IV}, to feed in and train our M-DNN. The first channel IO represents the radiometric values of the original SAR Images, the second and third channels are derived from IO; in particular, IW represents the output of the wind speed estimation using CMOD5 algorithm (Hersbach et al., 2003) and IV represents the variance of IO that incorporates texture information and at the same time encapsulates oil spill transition regions. IM channels were split and linearly transformed for data augmentation (rotation and reflection) to obtain a total of 80,772 sub-images of 224 × 224 pixels. From the entire database, 80 % of the sub-images were used in the DNN training process, the remaining (20 %) was used for testing our final architecture. Our experimental results show higher pixel-level classification accuracy when 2 or 3 channels are used in the M-DNN, reaching an accuracy of 98.56 % (the highest score reported in the literature for DNN models). Additionally, our M-DNN model provides fast training convergence rate (about 14 times better on the average than previous works), which proves the effectiveness of our proposed method. According to our knowledge, our work is the first multi-channel DNN based scheme for the classification of oil spills at different scales.

Unsupervised SAR Image Change Detection Based on Histogram Fitting Error Minimization and Convolutional Neural Network

Synthetic aperture radar (SAR) image change detection is one of the most important applications in remote sensing. Before performing change detection, the original SAR image is often cropped to extract the region of interest (ROI). However, the size of the ROI often affects the change detection results. Therefore, it is necessary to detect changes using local information. This paper proposes a novel unsupervised change detection framework based on deep learning. The specific method steps are described as follows: First, we use histogram fitting error minimization (HFEM) to perform thresholding for a difference image (DI). Then, the DI is fed into a convolutional neural network (CNN). Therefore, the proposed method is called HFEM-CNN. We test three different CNN architectures called Unet, PSPNet and the designed fully convolutional neural network (FCNN) for the framework. The overall loss function is a weighted average of pixel loss and neighborhood loss. The weight between pixel loss and neighborhood loss is determined by the manually set parameter λ. Compared to other recently proposed methods, HFEM-CNN does not need a fragment removal procedure as post-processing. This paper conducts experiments for water and building change detection on three datasets. The experiments are divided into two parts: whole data experiments and random cropped data experiments. The complete experiments prove that the performance of the method in this paper is close to other methods on complete datasets. The random cropped data experiment is to perform local change detection using patches cropped from the whole datasets. The proposed method is slightly better than traditional methods in the whole data experiments. In experiments with randomly cropped data, the average kappa coefficient of our method on 63 patches is over 3.16% compared to other methods. Experiments also show that the proposed method is suitable for local change detection and robust to randomness and choice of hyperparameters.

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

Deep learning based on Convolutional Neural Networks (CNN) has been widely applied in Synthetic Aperture Radar (SAR) target recognition and made significant progress. However, due to the physical effects of the equipment used to collect images, various degrees of speckle noise will be introduced into SAR images. Traditional CNN based SAR target recognition methods are premised on the same noise intensity in the training and testing set, which is contrary to the target recognition in practice. To alleviate this problem, we propose a novel speckle noise resistant framework for SAR target recognition, called Dual Consistency Alignment based Self-Supervised Learning(DCA-SSL). Firstly, original SAR images are randomly added to speckle noise with different thresholds through multiplicative noise, after which contrastive pre-training is performed on unlabeled data. During this period, we combine instance pseudo-label consistency alignment and feature consistency alignment to align multiple threshold speckle noise views with original views under the same targets. Finally, the pre-trained model is migrated to the downstream SAR speckle noise target recognition task. In this article, speckle noise modeling is conducted based on MSTAR data testing set, and experiment results reveal that this method can adapt to different intensities of speckle noise, is robust to modeled SAR image recognition and maintains a high recognition rate even in small sample learning. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Jordan-Liao/DCA-SSL</uri> .

SAR Image Compression Based on Multi-Resblock and Global Context

The synthetic aperture radar(SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this paper is to introduce a new framework for compressing SAR images. Firstly, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.

A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery

Rice is one of the most essential and strategic food sources globally. Accordingly, policymakers and planners often consider a special place in the agricultural economy and economic development for this essential commodity. Typically, a sample survey is carried out through field observations and farmers’ consultations to estimate annual rice yield. Studies show that these methods lead to many errors and are time-consuming and costly. Satellite remote sensing imagery is widely used in agriculture to provide timely, high-resolution data and analytical capabilities. Earth observations with high spatial and temporal resolution have provided an excellent opportunity for monitoring and mapping crop fields. This study used the time series of dual-pol synthetic aperture radar (SAR) images of Sentinel-1 and multispectral Sentinel-2 images from Sentinel-1 and Sentinel-2 ESA’s Copernicus program to extract rice cultivation areas in Mazandaran province in Iran. A novel multi-channel streams deep feature extraction method was proposed to simultaneously take advantage of SAR and optical imagery. The proposed framework extracts deep features from the time series of NDVI and original SAR images by first and second streams. In contrast, the third stream integrates them into multi-levels (shallow to deep high-level features); it extracts deep features from the channel attention module (CAM), and group dilated convolution. The efficiency of the proposed method was assessed on approximately 129,000 in-situ samples and compared to other state-of-the-art methods. The results showed that combining NDVI time series and SAR data can significantly improve rice-type mapping. Moreover, the proposed methods had high efficiency compared with other methods, with more than 97% overall accuracy. The performance of rice-type mapping based on only time-series SAR images was better than only time-series NDVI datasets. Moreover, the classification performance of the proposed framework in mapping the Shirodi rice type was better than that of the Tarom type.

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

Enhancement algorithm for separability of inland water body in synthetic aperture radar image via sparse representation and image fusion

ABSTRACT In synthetic aperture radar (SAR) images, inland water bodies generally appear as dark textures, but due to the interference of thermal noise, speckle noise, and azimuth blur, inland water bodies can be easily confused with weakly scattered areas such as roads, wetlands, and sand. Most of the existing denoising methods have been proposed for removing speckle noise. However, the presence of thermal noise and azimuth blur still makes it difficult to distinguish inland water bodies. The denoising method based on the Doppler spectrum can effectively remove thermal noise, speckle noise, and azimuth blur, but it reduces the resolution, which blurs the contour of the water body, and it is difficult to apply to the detection and processing of small inland water bodies. Fusing a noisy high-resolution image with a low-resolution image with less noise makes it possible to remove the speckle noise and thermal noise of the SAR image while maintaining the edge texture of the water body. Based on the idea of image fusion, sparse representation is used to fuse the original noisy SAR image (HR) and the low-precision image (LR) processed by the denoising algorithm to maximise the use of all the information in HR and LR. According to this idea, an enhancement algorithm for separability of inland water body based on sparse representation is proposed. The experimental results show that the proposed model significantly improves the separability of inland water bodies in SAR images.

Change Type Recognition for SAR Images Using Statistical Bidirectional LSTM Network With Few Labeled Samples

Change detection in synthetic aperture radar (SAR) images has garnered significant research interest. However, to date, only the change area can be obtained, which seriously restricts further development of SAR image applications. To solve this problem, this paper proposes a change type recognition method for SAR images based on a statistical bidirectional long short-term memory (LSTM) network. The proposed method can obtain the changed area and identify the land type change between pairs of SAR images. Considering the statistical characteristics and sample number of SAR images, change detection and type recognition are realized in a unified framework. First, feature extraction is performed through the statistical features of the original SAR image and difference image (DI). Then, a bidirectional LSTM network is used to identify the change types and obtain the change detection results under the small sample deep learning network. On two sets of medium-low resolution SAR image data and one set of GaoFen-3 high-resolution real SAR image data, our method produced high-quality results under small sample conditions.

Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

Target Recognition of SAR Images Based on SVM and KSRC.

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation-based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images.

A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.

Improving SAR Target Recognition Performance Using Multiple Preprocessing Techniques.

The synthetic aperture radar (SAR) image preprocessing techniques and their impact on target recognition performance are researched. The performance of SAR target recognition is improved by composing a variety of preprocessing techniques. The preprocessing techniques achieve the effects of suppressing background redundancy and enhancing target characteristics by processing the size and gray distribution of the original SAR image, thereby improving the subsequent target recognition performance. In this study, image cropping, target segmentation, and image enhancement algorithms are used to preprocess the original SAR image, and the target recognition performance is effectively improved by combining the above three preprocessing techniques. On the basis of image enhancement, the monogenic signal is used for feature extraction and then the sparse representation-based classification (SRC) is used to complete the decision. The experiments are conveyed on the moving and stationary target acquisition and recognition (MSTAR) dataset, and the results prove that the combination of multiple preprocessing techniques can effectively improve the SAR target recognition performance.

Combination of Joint Representation and Adaptive Weighting for Multiple Features with Application to SAR Target Recognition

For the synthetic aperture radar (SAR) target recognition problem, a method combining multifeature joint classification and adaptive weighting is proposed with innovations in fusion strategies. Zernike moments, nonnegative matrix factorization (NMF), and monogenic signal are employed as the feature extraction algorithms to describe the characteristics of original SAR images with three corresponding feature vectors. Based on the joint sparse representation model, the three types of features are jointly represented. For the reconstruction error vectors from different features, an adaptive weighting algorithm is used for decision fusion. That is, the weights are adaptively obtained under the framework of linear fusion to achieve a good fusion result. Finally, the target label is determined according to the fused error vector. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset under the standard operating condition (SOC) and four extended operating conditions (EOC), i.e., configuration variants, depression angle variances, noise interference, and partial occlusion. The results verify the effectiveness and robustness of the proposed method.

Synthetic aperture radar target recognition based on joint classification of selected monogenic components by nonlinear correlation information entropy

A synthetic aperture radar (SAR) target recognition method is proposed using monogenic components as basic features. The monogenic signal is employed to decompose original SAR images into multi-scale components. Considering the redundancy and possible indiscrimination in the monogenic components, the nonlinear correlation information entropy (NCIE) is adopted as the criteria for the selection of valid components. The subset of monogenic components with the highest NCIE is chosen and classified by joint sparse representation (JSR). Using the inner correlations of the selected components, JSR could improve the overall reconstruction precision thus enhancing the recognition performance. Experiments are proceeded on the moving and stationary target acquisition and recognition dataset under the standard operating condition and several extended operating conditions, including configuration variances, depression angle variances, noise corruption, and partial occlusion. The results validate the superior effectiveness and robustness of the proposed method over several existed SAR target recognition methods.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar (SAR) image de-noising method based on the local pixel grouping (LPG) principal component analysis (PCA) and guided filter is proposed. This method contains two steps. In the first step, we process the noisy image by coarse filters, which can suppress the speckle effectively. The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction. Then, we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching. The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation, so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain. In the second step, we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering. Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM) index and the equivalent number of looks (ENLs), and is of perceived image quality.