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.

Efficient extraction of spectral sequences and geospatial information is crucial in hyperspectral image (HSI) classification. RNNs and Transformers excel in capturing long-range spectral features, while CNNs excel in aggregating spatial information through convolutional kernels. However, RNNs and Transformers suffer from low computational efficiency, and CNNs have limitations in perceiving global contextual information. To address these issues, this paper proposes Graph-Mamba–an efficient graph structure learning vision Mamba for HSI classification. Specifically, GraphMamba is a novel hyperspectral information processing paradigm that preserves spatial-spectral features by constructing spatial-spectral cubes and employs a linear spectral encoder to enhance the operability of subsequent tasks. The core components of GraphMamba include the HyperMamba module, which enhances computational efficiency, and the SpatialGCN module, designed for adaptive spatial context awareness. The HyperMamba mitigates clutter interference by employing a global mask (GM) and introduces a parallel training and inference architecture to alleviate computational bottlenecks. Meanwhile, the SpatialGCN utilizes weighted multi-hop aggregation (WMA) for spatial encoding, emphasizing highly correlated spatial structural features. This approach enables flexible aggregation of contextual information while minimizing spatial noise interference. Notably, the encoding modules of the proposed GraphMamba architecture are both flexible and scalable, providing a novel approach for the joint mining of spatial-spectral information in hyperspectral images. Extensive experiments were conducted on three different scales of real HSI datasets. When compared with state-of-the-art classification methods, GraphMamba demonstrated superior performance. The core code will be released at https://github.com/ahappyyang/GraphMamba.

Due to the scarcity of prior information and the high complexity of spectral data, hyperspectral image (HSI) clustering presents a significant challenge. Although recent deep clustering methods have demonstrated remarkable performance, their intricate network structures and poor robustness hinder their practical application. To address this issue, we propose an efficient and lightweight spectral-spatial feature graph contrastive learning framework (S ² GCL) for robust HSI clustering. Specifically, we have designed a novel spectral-spatial feature encoder that fully leverages the information in HSI by incorporating both spatial structure and spectral similarity matrices. To establish a lightweight model, we implement several effective designs: First, S ² GCL eliminates the commonly used data augmentation and discriminator in GCL during the generation of positive embeddings. Second, we utilize a multilayer perceptron (MLP) to produce low-dimensional embeddings instead of relying on graph convolutional networks (GCN). Third, negative embeddings are generated through row-shuffling, avoiding the use of neural networks. Finally, we propose a multiple boundary loss function to extract complementary information from spatial structures and neighboring nodes, while also constraining the inter-class differences between positive and negative examples. We conducted extensive experiments on four publicly available datasets and compared S ² GCL with state-of-the-art clustering methods. The results indicate that S ² GCL achieves satisfactory performance. The code for S ² GCL will be released at https://github.com/ahappyyang/S2GCL.

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.