Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

Abstract

Aiming at the difficulty of obtaining sufficient labeled Hyperspectral image (HSI) data and the inconsistent feature distribution of different HSIs, a novel Unsupervised heterogeneous domain adaptation CycleGan (UHDAC) is proposed by using CycleGan to capture the transferable features in the absence of similar data. On the one hand, the two-way mapping is used to find the internal relationship between the source and target domain data, while the two-way adversary is used to constrain the source and target domain features, realizing the alignment of feature distributions. On the other hand, the CORAL loss function is introduced to minimize the distance between the second-order statistical difference between the source and target domain features, so as to solve the insufficient constraint of mapping relationship caused by the low consistency of HSI data structure in different domains. Experiments on three real HSI datasets show that UHDAC can effectively realize the unsupervised classification of target domain HSI with high classification accuracy by using the labeled HSI data in the source domain.