Disclosed is a hyperspectral remote sensing image classification method based on a self-attention context network. The method comprises: by means of self-attention learning and context encoding, constructing a spatial dependency between pixels in a hyperspectral remote sensing image, and extracting a global context feature. On hyperspectral remote sensing data contaminated by an adversarial attack, the method can still maintain superior ground object identification accuracy, thereby better satisfying security and reliability requirements of a hyperspectral remote sensing image classification task.
HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION METHOD BASED ON SELF-ATTENTION CONTEXT NETWORK