Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.

In remote sensing scene classification (RSSC), features can be extracted with different spatial frequencies where high-frequency features usually represent detailed information and low-frequency features usually represent global structures. However, it is challenging to extract meaningful semantic information for RSSC tasks by just utilizing high- or low-frequency features. The spatial composition of remote sensing images (RSIs) is more complex than that of natural images, and the scales of objects vary significantly. In this article, a multiscale feature fusion covariance network (MF ² CNet) with octave convolution (Oct Conv) is proposed, which can extract multifrequency and multiscale features from RSIs. First, the multifrequency feature extraction (MFE) module is used to obtain fine-grained frequency features by Oct Conv. Then, the features of different layers in MF ² CNet are fused by the multiscale feature fusion (MF ² ) module. Finally, instead of using global average pooling (GAP), global covariance pooling (GCP) extracts high-order information from RSIs to capture richer statistics of deep features. In the proposed MF ² CNet, the obtained multifrequency and multiscale features can effectively improve the performance of CNNs. Experimental results on four public RSI datasets show that MF ² CNet has advantages in RSSC over current state-of-the-art methods. The source codes of this method can be found at https://github.com/liuqingxin-chd/MF2CNet .

Most synthetic aperture radar (SAR) ship detectors based on convolutional neural networks (CNNs) needed preset anchor boxes to object classification and bounding box coordinate regression. However, the sparsity and unbalanced distribution of ships in SAR images mean that most anchor boxes are redundant. Thus, the anchor settings directly affect the performance and generalization ability of the detector. In addition, a variety in ship scales and the substantial interference of inshore backgrounds bring significant challenges to the SAR ship detector’s performance improvement. In this letter, a novel anchor-free-based detector, named feature balance and united attention (FBUA-Net), is proposed. We adopt a keypoint-based strategy to predict bounding boxes to eliminate the influence of anchors. Besides, we propose a global context-guided feature balanced pyramid (GC-FBP), which balances the semantic information at different levels of the feature pyramid by aggregation and averaging and uses the global context module (GCM) to learn global contextual information to construct long-range dependencies between ship targets and the background. Considering the interference of scattering noise to the detector, a united attention module (UAM) is designed to reduce the interference of surrounding noise by focusing on the spatial shape and scale size of ship targets in both the spatial and scale domains. Experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) datasets show that our detector achieves state-of-the-art (SOTA) performance. The source code can be found at https://github.com/so-bright/FBUA-Net .

The process of annotating hyperspectral image (HSI) data is characterized by its time-consuming and labor-intensive nature. To address this challenge, researchers often employ a meta-learning paradigm known as few-shot learning (FSL), which leverages source domains containing a substantial number of labeled samples to assist in the classification of target domains with limited labeled samples. Many existing FSL methods rely on a conditional domain-adversarial strategy to mitigate the domain shift between source and target domains. However, these methods overlook the fact that the degrees of conditional distribution discrepancies between the two domains can vary significantly across different classes, leading to suboptimal conditional distribution alignment. To address this problem, we propose a framework called Adaptive Domain-Adversarial Few-Shot Learning (ADAFSL). Overall, the proposed ADAFSL employs an adaptive strategy that assigns varying weights to the conditional adversarial losses for different classes based on their respective degrees of discrepancies, thereby achieving global conditional distribution alignment. Specifically, a local alignment score map is constructed by measuring the similarity between labeled and unlabeled samples using both Euclidean and class-covariance metrics. This map is then multiplied with the conditional adversarial loss map, thus allocating more emphasis to the classes exhibiting greater discrepancies between the two domains. Moreover, to enhance cross-domain FSL, we design a multi-scale spectral-spatial feature extraction (MSFE) module, which incorporates cascaded multi-scale dilated convolutions. Experimental results on four public HSI datasets demonstrate that the proposed ADAFSL outperforms other state-of-the-art methods. The source code of this method can be found at https://github.com/JieW-ww/ADAFSL.

ELLK-Net, an efficient, lightweight network with a large kernel, is proposed for SAR ship detection. It addresses background variations, different ship scales, and noise interference challenges. ELLK-Net uses an anchor-free detector framework and sequentially decomposes large kernel convolutions to capture comprehensive global information and long-range dependencies. It adaptively selects convolution kernels on the basis of target characteristics, enhancing multiscale feature expression. A novel large kernel multiscale attention module is introduced to enhance interlayer feature fusion and semantic alignment, mitigating the impacts of overlapping ships and scattering noise. Structural reparameterization techniques optimize inference speed across devices without compromising accuracy. The experimental results on the SSDD and HRSID datasets demonstrate that ELLK-Net achieves impressive AP ₅₀ values of 95.6% and 90.6% for horizontal box detection and 89.7% and 79.7% for rotating box detection, respectively. The reparameterized detector exhibits a significant 48.7% FPS improvement on the Nvidia Jetson NX platform, indicating its suitability for edge computing deployment. The code is available at https://github.com/CHD-IPAC/ELLK-Net.

Training a deep-learning classifier notoriously requires hundreds of labeled samples at least. Many practical hyperspectral image (HSI) scenarios suffer from a substantial cost associated with obtaining a number of labeled samples. Few-shot learning (FSL), which can realize accurate classification with prior knowledge and limited supervisory experience, has demonstrated superior performance in the HSI classification. However, previous few-shot classification algorithms assume that the training and testing data are distributed in the same domains, which is a stringent assumption in realistic applications. To alleviate this limitation, we propose a cross-domain FSL based on graph convolution contrast (GCC-FSL). The proposed method leverages cross-domain learning to acquire transferable knowledge from the source domain for classifying samples in the target domain. Specifically, a positive and negative pairs module is designed for constructing positive and negative pairs by matching the class prototypes of the target domain with those of the source domain, which aligns the data distribution of the source and target domains. In addition, a graph convolution contrast (GCC) module is proposed for extracting global graph-structure information of HSI to improve the ability of feature expression and constructing a graph-contrast loss to solve a domain-shift problem. Finally, a multiscale feature extraction network is designed to expand convolutional receptive fields through feature reuse and increase information interaction for fine-grained feature extraction. The experimental results demonstrate the improved performance for the proposed FSL framework relative to both state-of-the-art convolutional neural network (CNN)-based methods as well as other few-shot techniques. The source code of this method can be found at https://github.com/JieW-ww/GCC-FSL .