Adaptive Smooth Adversarial Learning for Cross-domain Hyperspectral Image Classification

Cross-domain classification has emerged as a widely employed method in remote sensing due to the challenges associated with obtaining labels for hyperspectral image (HSI). To address the impact of inter-domain differences on classification performance, domain-adaptive deep learning (DL) methods have proven effective in recent years. However, many existing methods struggle to effectively integrate high-dimensional information in hyperspectral cross-scene classification, are insufficient for inter-class discriminability, and are sensitive to small perturbations. These limitations hinder the accurate extraction of meaningful features across different domains. To tackle these issues, we propose a cross-scene HSI classification method named Adaptive Smooth Adversarial Learning (ASAL). This approach utilizes a dynamic attention mechanism to capture region semantic information, where the spectral attention deformable block (SADB) is designed to achieve dense pixel-level attention. Starting from the concept of class confusion, combining category minimization with multi-scale analysis, we design Entropy-weighted Minimum-class Confusion (EMC) loss, which improves the ability to capture inter-class relationships. The sharpness of the loss function is also reduced using a double smoothing regularization strategy, which enhances the generalization ability of the model. Experimental results show the proposed method improves accuracy by about 3.00 % or higher over state-of-the-art methods across four cross-scene HSI benchmarks. The code for the proposed network will be available at https://github.com/liurongwhm.