Domain-Invariant Feature and Generative Adversarial Network Boundary Enhancement for Multi-Source Unsupervised Hyperspectral Image Classification

Abstract

Hyperspectral image (HIS) classification, a crucial component of remote sensing technology, is currently challenged by edge ambiguity and the complexities of multi-source domain data. An innovative multi-source unsupervised domain adaptive algorithm (MUDA) structure is proposed in this work to overcome these issues. Our approach incorporates a domain-invariant feature unfolding algorithm, which employs the Fourier transform and Maximum Mean Discrepancy (MMD) distance to maximize invariant feature dispersion. Furthermore, the proposed approach efficiently extracts intraclass and interclass invariant features. Additionally, a boundary-constrained adversarial network generates synthetic samples, reinforcing the source domain feature space boundary and enabling accurate target domain classification during the transfer process. Furthermore, comparative experiments on public benchmark datasets demonstrate the superior performance of our proposed methodology over existing techniques, offering an effective strategy for hyperspectral MUDA.