Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification

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…

• MD
  Ministerio de Ciencia e Innovación