Synthetic aperture radar (SAR) ship detection especially for small ships has issues, such as dense distribution of ships, interference from land and small islands. To address these issues, many deep learning methods, including anchor-based and anchor-free methods, have been successfully migrated from optical scenes to SAR images. However, when the preset scale of anchors does not match well with the ships, it will seriously reduce the detection precision. Due to the lack of anchor-based refinement process, anchor-free methods may generate missing or false alarms in complex scenarios. In this article, a two-stage ship detection network which can generate anchors is proposed. First, our method generates high-quality anchors by network, which is more beneficial for the network to capture small ships. In addition, the generated anchors are centrally set in the region of ships, which reduces the number of anchors unrelated to ships. Second, the receptive field enhancement module is inserted into the feature pyramid network. It sets different dilation ratios of atrous convolution according to the scale of the feature map, which further enriches the semantic information of the elements in the feature map. Therefore, the network can use the information of a wider region effectively to detect ships. Finally, to verify the effectiveness of our method, extensive experiments are carried out on SAR ship detection dataset and high-resolution SAR images dataset. The results show that our method has more strong ability of detecting small ships, and achieves better detection performance than some state-of-the-art methods.

The advancement of convolutional neural networks (CNNs) has greatly promoted the development of ship detection in SAR images. The oriented ship detection is practical and imperative, but the development of its fully-supervised methods is limited by the insufficiency of data annotated in oriented bounding box (OBB). Therefore, we proposed a weakly supervised learning (WSL) paradigm to train network by only data annotated in horizontal bounding box (HBB) for oriented ship detection. The paradigm follows a coarse-to-fine prediction route, and the ships coarse orientations and high-quality pseudo-labels both mined from images are used to train the network combined with the HBB annotations. The designed orientation initialization decoder (OID) utilizes the output from the prediction branch of ship coarse orientation (PBSCO) and the regression branch to decode coarse OBBs, based on the horizontal proposals, and then these OBBs are refined by the fine prediction head, which is jointly trained by high-quality pseudo-labels and HBB annotation. Extensive experiments were conducted on the SAR ship detection dataset (SSDD) and the high resolution SAR images dataset (HRSID) with both OBB and HBB annotations, and the results show that our proposed WSL paradigm can train network to attain the AP ₅₀ and R in the same level of mainstream fully-supervised methods in remote sensing.

The synthetic aperture radar (SAR) oriented ship detection is a more complex and finer task than the horizontal, and it provides ship orientation for military and civilian applications, such as trajectory prediction. However, many oriented target detection methods discarded high-resolution (low-level) feature map, which provides morphological features for predicting accurate bounding box for small ships, due to computational cost and lack of semantic information. Additionally, most methods follow the samples assignment in horizontal detection after padding oriented bounding box. The inaccurate geometric relationship causes that some low-quality positive samples without contributing accuracy improvement are introduced. To improve the precision of small oriented ship detection, we proposed the feature pyramid network contained high-resolution feature (FPNHRF) to fuse high-level semantics and highlight contours and textures of ships in the low-level feature map. To select high-quality positive rotated samples, dynamic rotated positive samples mining (DRPSM) is designed to mine hard positive samples for oriented ship detection specially. In one word, a two-stage and anchor-free detector trained by DRPSM and utilized high-resolution feature map is proposed for detecting oriented ships in SAR imagery. Subsequently, extensive experiments have been conducted on two oriented SAR datasets: the SSDD and the HRSID. As a result, our method attains Recall of 96.2% and 90.1%, and AP50 of 90.7% and 86.5% on SSDD and HRSID respectively. It shows that our detector achieves state-of-the-art results applied in capturing ships, when compared with other popular remote sensing oriented detectors.

With the rapid development of deep learning, the convolutional neural networks (CNNs) have been widely used in hyperspectral image classification (HSIC) and achieved excellent performance. However, CNNs reuse the same kernel weights over different locations, thus resulting in the insufficient capability of capturing diversity spatial interactions. Moreover, CNNs usually require a large amount of training samples to optimize the learnable parameters. When training samples are limited, the classification performance of CNN tends to drop off a cliff. To tackle the aforementioned issues, a novel residual spatial attention kernel generation network (RSAKGN) is proposed for HSIC. First, a spatial attention kernel generation module (SAKGM) is built to extract discriminative semantic features, which can dynamically calculate the attention weights to generate specific spatial attention kernels over different locations. Then, we combine the SAKGM with residual learning framework by embedding the SAKGM into a bottleneck residual block to obtain the residual spatial attention block (RSAB). The RSAKGN is constructed by stacking several RSABs. Experimental results on three public HSI datasets demonstrate that the proposed RSAKGN method outperforms several state-of-the-arts with small sample size.

The resolution of nonscanning three-dimensional (3-D) imaging systems is limited by the number and accuracy of the array sensors. Moreover, for space-continuous targets in practical situations, the echo pulses in time-of-flight systems overlap and traditional peak detection is no longer suitable for super-resolution applications. Hence, compressive sensing (CS) has been introduced to achieve super-resolution. However, most of the conventional CS algorithms cannot be used directly for 3-D image reconstruction. In this paper, we propose a novel super-resolution algorithm for nonscanning 3-D laser imaging based on CS reconstruction. To acquire the range information of space-continuous targets, an all-one projection is implemented in advance to estimate the spatial distribution of the targets; a range observation matrix composed of time-interval basis vectors is then constructed to obtain the peak values of each frame from overlapping echo pulses. Because of the spatial continuity of the targets, Tikhonov regularization is utilized to solve the ill-posed inverse problem. Furthermore, to enhance the reconstruction quality of the adjacent frames, multihypothesis prediction is used with displacement and diffusion models to estimate the motion of the contour line. Simulation results based on real data from the ASTER global digital elevation model demonstrate the effectiveness and high accuracy of the proposed algorithm for complex landforms.