Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

In existing superpixel-wise segmentation algorithms, superpixel generation most often is an isolated preprocessing step. The segmentation performance is determined to a certain extent by the accuracy of superpixels. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and merging steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the single-polarization synthetic aperture radar (SAR) image. It outputs the statistical likelihood that each pixel belongs to different superpixels. In superpixel merging part, as one of our main contributions, we propose a superpixel-wise statistical dissimilarity measure method for converting the soft superpixels set into a self-connected weighted graph. More importantly, inspired by the concept of the number of walks in graph theory, we define the $k$ -order connectivity of each vertex. This definition can intelligently indicate the potential soft cluster centers and class assignments in graph. This merging method is differentiable, computationally simple, and free of empirical parameters. The superpixel generation and merging phases can be implemented under a unified deep network. The benefit is that our method can iteratively adjust the shapes of the superpixels according to the boundaries and segmentation results during training, until the satisfactory segmentation results are captured. Experimental results on real SAR images demonstrate that the segmentation precision of our proposed method is superior to other state-of-the-art methods in terms of precision and computational efficiency.

Since the number of superpixels is lower than that of pixels, superpixels can substantially speed up subsequent processing steps and have been widely used in synthetic aperture radar (SAR) image segmentation. However, in most of the existing superpixel-wise segmentation algorithms, superpixel prediction is an isolated preprocessing step and is independent of the segmentation task. The performance of the segmentation results is determined by the accuracy of superpixels. Once superpixels are generated, their shape cannot be changed in the following segmentation stage, even if the same superpixels contain pixels of different landcovers. To address this, we propose an end-to-end trainable superpixel-wise segmentation method for single-polarization SAR images. First, we design a differentiable boundary-ware clustering method for estimating task-specific superpixels. Instead of the hard association between pixels and superpixels in the existing superpixel algorithms, this method introduces the soft association map to make the clustering differentiable. Hence, it can be implemented using a simple deep fully convolutional network. In the segmentation part, we propose a novel soft graph convolution network (Soft-GCN), which takes the association map as input and performs superpixel-wise segmentation. The advantage of our method is that superpixel generation and graph convolution parts can be trained under a unified framework, until two parts obtain the optimum parameters. In the training process, it can adaptively adjust the shape of the superpixels according to the segmentation results, ensuring the superpixels correctly adhere the boundaries. Experimental results with simulated and real SAR images demonstrate that our method outperforms other state-of-the-art segmentation algorithms, while also being faster.

Automatic target recognition (ATR) has made great progress with the development of deep learning. However, the target feature in synthetic aperture radar (SAR) image is not consistent with human vision, and the SAR training samples are always limited. These hard issues pose new challenges to the SAR ATR based on convolutional neural network (CNN). In this letter, we propose an improved CNN model to solve the limited sample issue via the feature augmentation and ensemble learning strategies. Normally, the high-level features that are more comprehensive and discriminative than the middle-level and low-level features are always employed for category discrimination. In order to make up the insufficient training features in the limited sample case, the cascaded features from optimally selected convolutional layers are concatenated to provide more comprehensive representation for the recognition. To take full advantage of these cascaded features, the ensemble learning-based classifier, namely, the AdaBoost rotation forest (RoF), is introduced to replace the original softmax layer to realize a more accurate limited sample recognition. Through the AdaBoost RoF method, not only are these features further enhanced by the rotation matrix but also a strong classifier is constructed by several weak classifiers with different adjusted weights. The experimental results on MSTAR data set show that the cascaded features and ensemble weak classifiers can fully exploit effective information in limited samples. Compared with the existing CNN method, the proposed method can improve the recognition accuracy by about 20% under the condition of ten training samples per class.

In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of saliency maps is determined by the accuracy of superpixels to a certain extent. However, it is still a challenge to develop a stable superpixel generation method. In this paper, we attempt to incorporate the superpixel generation and saliency calculation steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the SAR images, which outputs the possibility that the pixels assigned to neighbor superpixels (soft superpixel). In saliency calculation part, as one of our main contributions, we propose a differentiable and computationally simple saliency model, i.e., lacunarity cue. It is inspired by the fact that generally the backscattering intensity of regions of interest (ROIs) in SAR images irregularly fluctuates. While the areas with consistent pixels are often ignored as the clusters. We improve the pixel-wise box differential dimension algorithm to measure the irregularity of scattering points in a superpixel. The superpixel generation and saliency calculation can be implemented under a unified deep network. Hence the shapes of the superpixels can be iteratively adjusted according to the saliency maps until the ROIs are correctly detected. Experiments on real SAR images with different sizes and scenes show that the saliency maps can effectively highlight the target areas, outperforming the state-of-the-art saliency detection models.

SAR ship detection is an important technology supporting water traffic monitoring and marine safety maintenance. In recent years, many methods based on deep neural networks have been used to improve the performance of SAR ship detection. These methods mainly focus on two issues: one is the false alarm of ship detection in complex inshore environments, and the other is the effective extraction and utilization of SAR ship features. The topic discussed in this paper is one of the culprits that has caused the aforementioned two problems, but has long been overlooked. Specifically, it pertains to the issue of intra-class imbalance in SAR ship detection. There are imbalances in the size distribution, azimuth distribution, and background distribution under the real data collection environment. However, since SAR ship detection is a single-class detection task, the aforementioned imbalances lack reliable descriptors during training. This paper proposes using evidence learning to obtain the epistemic uncertainty as a descriptor of biased learning on samples. Contrastive learning is used to further utilize the uncertainty label of samples to correct biased learning under intra-class imbalance. The proposed method is proven to be effective on multiple network models. AP50 reaches 94.8% on the HRSID dataset, 98.4% on SSDD dataset and 80.9% on the LS-SSDD dataset, both achieving SOTA performance.