Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification 论文

详细信息

发表期刊/会议

IEEE Transactions on Geoscience and Remote Sensing

发表日期

2023-01-01

发表年份

2023

摘要

In recent years, convolutional neural networks (CNNs) have drawn significant attention for the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the vision transformer (ViT) provides promising classification performance compared to CNNs. Many researchers have incorporated ViT for HSI classification purposes. However, its performance can be further improved because the current version does not use spatial–spectral features. In this article, we present a new morphological transformer (morphFormer) that implements a learnable spectral and spatial morphological network, where spectral and spatial morphological convolution operations are used (in conjunction with the attention mechanism) to improve the interaction between the structural and shape information of the HSI token and the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CLS</monospace> token. Experiments conducted on widely used HSIs demonstrate the superiority of the proposed morphFormer over the classical CNN models and state-of-the-art transformer models. The source will be made available publicly at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mhaut/morphFormer</uri> .