Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating

TL;DR

This paper introduces STNet, a novel hyperspectral image classification network that uses spectral-spatial attention decoupling and adaptive gating to improve feature extraction, reduce overfitting, and outperform existing methods.

Contribution

The paper proposes a dual attention decoupling and gating mechanism in a Transformer-based network for hyperspectral image classification, enhancing feature fusion and generalization.

Findings

Outperforms mainstream methods on IN, UP, and KSC datasets.

Effectively reduces overfitting in small-sample and noisy scenarios.

Enhances feature extraction without increasing network complexity.

Abstract

Deep neural networks face several challenges in hyperspectral image classification, including high-dimensional data, sparse distribution of ground objects, and spectral redundancy, which often lead to classification overfitting and limited generalization capability. To more effectively extract and fuse spatial context with fine spectral information in hyperspectral image (HSI) classification, this paper proposes a novel network architecture called STNet. The core advantage of STNet stems from the dual innovative design of its Spatial-Spectral Transformer module: first, the fundamental explicit decoupling of spatial and spectral attention ensures targeted capture of key information in HSI; second, two functionally distinct gating mechanisms perform intelligent regulation at both the fusion level of attention flows (adaptive attention fusion gating) and the internal level of feature transformation (GFFN). This characteristic demonstrates superior feature extraction and fusion capabilities compared to traditional convolutional neural networks, while reducing overfitting risks in small-sample and high-noise scenarios. STNet enhances model representation capability without increasing network depth or width. The proposed method demonstrates superior performance on IN, UP, and KSC datasets, outperforming mainstream hyperspectral image classification approaches.