Unsupervised Domain Adaptation With Dense-Based Compaction for Hyperspectral Imagery

Abstract

Enormously hard work of label obtaining leads to the lack of enough annotated samples in the hyperspectral imagery (HSI). The mentioned reality inferred the unsupervised classification performance barely satisfactorily. Unsupervised domain adaptation is exploited for knowledge delivery from a labeled source domain to boost the performance on an unlabeled target domain. In this article, we propose an unsupervised domain adaptation architecture with dense-based compaction (UDAD) for HSI classification (HSIC). The processes of spectral–spatial feature compaction, unsupervised domain adaptation, and classifier training are incorporated with an integrated framework to complete the HSI cross-scene classification. The core of the proposed framework is to utilize adversarial domain learning to reduce the domain discrepancy. To this end, the classifier trained in the source domain would accomplish well in the target domain for the unsupervised HSIC. Besides, to extract the discriminative spectral–spatial feature for the HSI domains, a dense-based compaction network is applied to complete the semisymmetric mapping. Our experiments illustrate that the UDAD model yields more effective classification performance than other state-of-the-art unsupervised HSIC methods.