MS3Net: Multiscale stratified-split symmetric network with quadra-view attention for hyperspectral image classification.

• 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]