Masked Self-Distillation Domain Adaptation for Hyperspectral Image Classification

Abstract

Deep learning-based unsupervised domain adaptation (UDA) has shown potential in cross-scene hyperspectral image (HSI) classification. However, existing methods often experience reduced feature discriminability during domain alignment due to the difficulty of extracting semantic information from unlabeled target domain data. This challenge is exacerbated by ambiguous categories with similar material compositions and the underutilization of target domain samples. To address these issues, we propose a novel masked self-distillation domain adaptation (MSDA) framework, which enhances feature discriminability by integrating masked self-distillation (MSD) into domain adaptation. A class-separable adversarial training (CSAT) module is introduced to prevent misclassification between ambiguous categories by decreasing class correlation. Simultaneously, CSAT reduces the discrepancy between source and target domains through biclassifier adversarial training. Furthermore, the MSD module performs a pretext task on target domain samples to extract class-relevant knowledge. Specifically, MSD enforces consistency between outputs generated from masked target images, where spatial-spectral portions of an HSI patch are randomly obscured, and predictions are produced based on the complete patches by an exponential moving average (EMA) teacher. By minimizing consistency loss, the network learns to associate categorical semantics with unmasked regions. Notably, MSD is tailored for HSI data by preserving the samples’ central pixel and the object to be classified, thus maintaining class information. Consequently, MSDA extracts highly discriminative features by improving class separability and learning class-relevant knowledge, ultimately enhancing UDA performance. Experimental results on four datasets demonstrate that MSDA surpasses the existing state-of-the-art UDA methods for HSI classification. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Li-ZK/MSDA-2024</uri>.