Oil spill detection in SAR images using multiscale normalized cut segmentation

ABSTRACTThe speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.

Ship detection from synthetic aperture radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep-learning-based detection methods. Inspired by the idea of domain adaptation, in this article, we propose a hierarchical similarity alignment neural network (HSANet) for ship detection in SAR images, which is a domain adaptive (DA) approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, structural alignment module (SAM) and prototype alignment module (PAM), are designed to, respectively, conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relationship between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.

Detection of distributed targets such as internal wave or ship wake on sea surface in Synthetic Aperture Radar (SAR) images is an important application of ocean microwave remote sensing. The chaotic characteristic of sea clutter gives some clues to targets detection on sea surface. Speckle, the inherent noise of SAR image, will affect the predictive accuracy of sea clutter adversely and reduce the detection performance of radar. In order to apply the chaotic characteristic of sea clutter to targets detection in SAR images more effectively, an improved chaos-based detection method is proposed in this paper. First, speckle noise is suppressed by undecimated wavelet transform (UWT), and then targets are detected on the basis of the chaotic characteristic of sea clutter from the denoised SAR images. Experimental results prove that the method proposed in this paper is effective for the distributed targets detection in SAR ocean images.

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Ship detection in synthetic aperture radar (SAR) images has attracted extensive attention due to its promising applications. While numerous methods for ship detection have been proposed, detecting ships in complex scenarios remains challenging. The main factors contributing to the lower detection accuracy are SAR image characteristics, such as blurred outlines, and similar scattering intensities between actual ship targets and background environment, induced by the special imaging mechanism. To alleviate these issues, we propose a Mixed-scale Self-distillation Network (MSNet) for accurate ship detection in SAR images. Firstly, the zoom strategy is used to obtain more ship target information, and differentiated information between ship targets and background environments at different scales is aggregated through the designed search module. Then, the consistency self-distillation module is proposed to match feature attention maps at different scales, which forces the model to capture the potential semantic attributes of ship targets through a self-distillation fashion. After that, the refinement module is developed to further enhance the discriminative semantics among different hierarchical features under mixed scales. Furthermore, to alleviate the uncertainty arising from indistinguishable background interference in SAR images, we introduce an uncertainty perception loss to facilitate the model to make accurate judgments in candidate regions. Extensive experiments are performed on the SAR ship detection dataset from the Gaofen-3, RadarSat-2, Sentinel-1, and TerraSAR satellites. The experimental results consistently demonstrate the superiority of our method over the existing state-of-the-art methods. Besides, detailed model analysis experiments further validate the effectiveness of our proposed method in SAR image ship detection tasks. The code will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shuangliumax/MSNet/</uri>

An efficient dark spot detection method for offshore oil spill in SAR images based on edge-enhanced attention fusion.

Airplane detection in synthetic aperture radar (SAR) images has drawn much attention owing to the success of deep learning methods. However, the development of fine-grained airplane detection in SAR images is still in a dilemma due to the small inter-class variance and the large intra-class variance in complex scenes with strong interference from the background. In addition, the class imbalance problem in multi-class fine-grained airplane recognition also significantly limits the direct application of general deep-learning-based airplane detectors. This paper proposes two effective methods to tackle the above two problems, respectively. First, we propose a sparse attention-guided fine-grained pyramid (SA-FP) module to simultaneously sample discriminative local features scattered in multi-scale layers and adaptively aggregate them with fine-grained attention to better classify subordinate-level airplanes with multiple scales. Second, a simple class-balanced copy-paste data augmentation (CC-DA) strategy, which randomly copies an airplane of one category and pastes it onto an image according to the class-wise probability, is proposed for class balance. Finally, extensive experiments on one public dataset and three representative deep-learning-based detection benchmarks are conducted to show the effectiveness and generalization of the two proposed methods. The combination of these two methods based on the Cascade R-CNN benchmark also won fifth place in fine-grained airplane detection in SAR images in the 2021 Gaofen challenge.

The multiscale normalized cut technique is used to detect land/sea boundary in synthetic aperture radar (SAR) images. The proposed method is a region-based one, which is able to capture and utilize spatial information in the image. The real SAR images, e.g. ERS, ENVISAT and COSMO-SkyMed (CSK) SAR images, together with the Landsat-7 ETM+ optical data and in-situ GPS data were collected and used to validate the performance of the proposed method for coastline detection in SAR images.

Fisher vectors (FVs) can capture multiple order information from superpixels (SPs) in synthetic aperture radar (SAR) images. Existing FV-based ship detectors mainly exploit the local contrast of FVs (LCFVs) but do not consider their global density features. This may lead to degraded performance in terms of discrimination between ship targets and the complex sea clutter. In this article, two new global cues from FVs are designed based on the fact that target FVs exhibit much lower densities than those of clutter FVs and also have large distances to the latter. Our two new global cues can suppress the sea clutter and significantly enhance ship targets throughout the SAR image. We also design an improved local cue from FVs for ship detection, in which the intensity contrast of SPs is incorporated into the existing LCFV indicator to reduce false alarms. By fusing the above two new global cues (and an improved local cue from FVs), we propose a new method for ship detection in SAR images. Experimental results based on Gaofen-3 SAR images show that the newly proposed detector provides better detection performance than other state-of-the-art detectors, especially in the presence of strong and highly heterogeneous sea clutter.

Independence of Synthetic Aperture Radar (SAR) images from weather and sun illumination helps us in maritime surveillance like an oil spill, to identify the illegal fishery activity and unauthorized marine vessels. Detection of marine vessels is like a point target detection in coarse to moderate resolution images. Detection of point targets is adversely affects by speckle noise. Object detection in SAR images, without explicitly reducing the speckle noise is one of the challenging task. In this paper two algorithms are presented, which show how improvement in the power of point target lead us to reduced number of false alarms. The first algorithm has three parts. First part uses Grave matrix, which is a ( $2\times 2$ ) Hermitian power matrix, generated by the multiplication of Sinclair matrix and conjugate of Sinclair matrix. Second part uses discrimination criteria to discriminate between clutter and vessels based on eigenvalue of the Grave matrix (G). The third part, fill the gaps by using morphological dilation. The only difference between the first and the second algorithms is that, in the second algorithm we uses Sinclair matrix (S2) instead of Grave matrix. Both the algorithms tested on two full-polarization (HH, HV, VH, VV) datasets and the results show the importance of scattering power matrix (G) as compared to scattering matrix (S2) for point target detection. The first data set is of size $498^{\ast}498$ captured by AlOS-1 PALSAR L-band data, covering the coastal region of Singapore. The second data set is of size $472^{\ast}472$ , covering the coast of Vancouver acquired in C-band by Radarsat-2.

In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.

Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial-temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results.

A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

Generative knowledge transfer for ship detection in SAR images

Synthetic Aperture Radar (SAR) is widely used in ship target detection because of its ability to operate under various weather conditions. However, some target images are difficult to obtain and label, resulting in a small sample size, which limits the development of target detection. Aiming at the problem of ship target detection in small sample SAR images, domain knowledge is used to revise and enhance the basic model in this paper. Firstly, a lightweight convolutional neural network model more suitable for small-sample SAR image ship target classification is proposed, involving target pixel and aspect ratio as domain knowledge to correct the classification results. Then, for ship target detection in SAR images, the acquisition method of scale class domain knowledge is improved. Moreover, texture-related domain knowledge based on the gray level co-occurrence matrix is extracted and used as classification features for the model to revise the model. Finally, Marine Targets Classification Dataset (MTCD) and Marine Targets Detection Dataset (MTDD) are briefly introduced and adjusted to meet the requirements of this research through screening and modification. The test results on balanced MTDD and small-sample datasets demonstrate the effectiveness of domain knowledge in improving network performance.