Transformer has been widely used in classification tasks for hyperspectral images (HSI) in recent years. Because it can mine spectral sequence information to establish long-range dependence, its classification performance can be comparable with the convolutional neural network (CNN). However, both CNN and Transformer focus excessively on spatial or spectral domain features, resulting in an insufficient combination of spatial-spectral domain information from HSI for modeling. To solve this problem, we propose a new end-to-end graph convolutional network (GCN) and Transformer fusion network with the spatial-spectral feature extraction (GTFN) in this paper, which combines the strengths of GCN and Transformer in both spatial and spectral domain feature extraction, taking full advantage of the contextual information of classified pixels while establishing remote dependencies in the spectral domain compared with previous approaches. In addition, GTFN uses Follow Patch as an input to the GCN and effectively solves the problem of high model complexity while mining the relationship between pixels. It is worth noting that the spectral attention module is introduced in the process of GCN feature extraction, focusing on the contribution of different spectral bands to the classification. More importantly, to overcome the problem that Transformer is too scattered in the frequency domain feature extraction, a neighborhood convolution module is designed to fuse the local spectral domain features. On Indian Pines, Salinas, and Pavia University datasets, the overall accuracies (OAs) of our GTFN are 94.00%, 96.81%, and 95.14%, respectively. The core code of GTFN is released at https://github.com/1useryang/GTFN.

Existing ship detection approaches in SAR images often suffer from inadequate learning of the detector and sub-optimal detection performance. To this end, Based on self-supervised contrastive learning, this letter consider using the relationship between samples to develop a more effective training strategy. First, the Instance-based RoI encode head is proposed, named InsRen head, a simple yet effective network structure. Its purpose is to encode the samples into a contrastive feature space, facilitating the measurement of contrastive learning. Furthermore, to adapt contrastive learning to ship detection, we have redefined some basic terms, such as query, positive key, and negative key, which can help the model build the training pipeline. Finally, we design the Instance-based Contrastive loss that does not require label supervision, named InsCon loss. With the penalty of the InsCon loss, the queries and positive key can learn more similar representations in the contrastive feature space. Simultaneously, the query and negative key are as far away as possible to increase the difference. With the help of InsRen head and InsCon loss, the training of the detection model is more effective. Experimental results demonstrate the superiority of our method.

There are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7–4.9% based on different backbone networks, and the detection performance is better than other advanced algorithms.

With the wide application of synthetic aperture radar in maritime surveillance, a ship detection method has been rapidly developed. However, there is still a key problem common in most methods, i.e., how to select positive and negative samples. The mainstream MaxIoUAssign has inherent problems, such as a fixed threshold and rough classification, resulting in the low quality of the positive samples. To solve these problems, we propose a new sample selection method called adaptively center-shape sensitive sample selection. The proposed method introduces shape similarity between proposal boxes and ground truth as one of the evaluation criteria and collaborates with intersection of union (IoU) to measure the quality of the proposal boxes. Meanwhile, the center distance between proposal boxes and ground truth is used to control the influence degree of IoU and shape similarity. In this way, the quality score of the proposal boxes can be determined through IoU, shape similarity, and center position, making sample selection more comprehensive. Additionally, to avoid a fixed threshold, the standard deviation of the quality score is used as a variable to form the adaptive threshold. Finally, we conducted extensive experiments on the benchmark SAR ship detection dataset (SSDD) and high-resolution SAR images datasets (HRSID) datasets. The experimental results demonstrated the superiority of our method.