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In recent years, the application of convolutional neural networks (CNNs) and graph convolutional networks (GCNs) in hyperspectral image classification (HSIC) has achieved remarkable results. However, the limited label samples are still a major challenge when using CNN and GCN to classify hyperspectral images. In order to alleviate this problem, a double branch fusion network of CNN and enhanced graph attention network (CEGAT) based on key sample selection strategy is proposed. First, a linear discrimination of spectral inter-class slices (LD_SICS) module is designed to eliminate spectral redundancy of HSIs. Then, a spatial spectral correlation attention (SSCA) module is proposed, which can extract and assign attention weight to the spatial and spectral correlation features. On the graph attention (GAT) branch, the HSI is segmented into some super pixels as input to reduce the amount of network parameters. In addition, an enhanced graph attention (EGAT) module is constructed to enhance the relationship between nodes. Finally, a key sample selection (KSS) strategy is proposed to enable the network to achieve better classification performance with few labeled samples. Compared with other state-of-the-art methods, CEGAT has better classification performance under limited label samples. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) exhibit excellent performance in hyperspectral image classification (HSIC) and have attracted significant interest. Nevertheless, the common CNN-based classification techniques still suffer from the following drawbacks. 1) Although deep CNNs can effectively extract features from hyperspectral images, a network that is too deep often leads to negative effects such as overfitting, vanishing gradients, and decreased accuracy. 2) Most of these models for HSIC do not fully consider the strong complementarity and correlation between features at different levels. To address these issues, firstly, a self-regularization parameter correction non-monotonic activation function, called Lush, was first proposed. Then, this study proposes a novel bias update method that can update network bias parameters more efficiently. On the basis of the above, a Lush multi-layer feature fusion polarization network (LMFFBNet) for hyperspectral image classification was proposed in this study. It designs a new after-melting technology to fuse deep and shallow features in multiple stages and layers, which can obtain more discriminative features, thereby improving the classification performance of hyperspectral images. Extensive experiments on five challenging datasets (Indian Pines, Pavia University, Kennedy Space Center, Salinas Valley, and Houston) have demonstrated that compared to some state-of-the-art methods, the proposed LMFFBNet method can provide better classification performance for hyperspectral images and has strong robustness. This fully proves the effectiveness of the LMFFBNet method. [ABSTRACT FROM AUTHOR]

• MS3Net is designed with a dual-stream symmetric structure for HSI classification. • Stratified-split module can promote the extraction of multiscale features. • Quadra-view attention can focus on valuable features from different dimensions. • Experimental results on four HSI datasets show the effectiveness of our method. Recently, hyperspectral image (HSI) classification has become a promising research direction in remote sensing image processing. Many HSI classification methods have been proposed based on convolutional neural networks (CNNs) and attention mechanisms (AMs). However, most current CNN-based methods only consider extracting features at a single scale in HSI, which may ignore the delicate features of some objects. Moreover, present AMs primarily focus on one feature dimension, such as spatial or channel attention, while disregarding dimension interaction. To conquer the above issues, a novel multiscale stratified-split symmetric network with quadra-view attention, namely MS3Net, is proposed for HSI classification. Generally, the proposed MS3Net has a dual-stream symmetric pipeline, which can better extract HSI's spectral signatures and spatial features. Specifically, the proposed MS3Net consists of three modules: a multiscale feature extraction module, a feature enhancement module, and a feature fusion module. Firstly, a stratified-split module is designed to extract multiscale spectral and spatial features. In addition, to reduce the complexity of the model, we designed pseudo-3-D spectral and spatial convolution to replace the traditional 3-D convolution operation. Secondly, a novel quadra-view attention module is proposed, guiding the model to focus on important features from multiple dimensions. Finally, the selective kernel feature fusion module is introduced, which can dynamically integrate spectral and spatial features. Experimental results on four benchmark HSI datasets with different scenes and resolutions confirm the visual and quantitative superiority of the proposed MS3Net over the state-of-the-art related methods in this research direction. [ABSTRACT FROM AUTHOR]