Cross-Scene Hyperspectral Image Classification Based on Deep Conditional Distribution Adaptation Networks

Abstract

Cross-scene classification of hyperspectral image (HSI) has been increasingly researched due to its crucial utilization in practical applications. However, cross-scene data generally perform distribution discrepancy, which hampers the transfer learning performance. To address this issue, deep conditional distribution adaptation networks (DCDAN) are proposed for HSI cross-scene classification, which aim to reduce the distribution shift between a source domain and a target domain. The proposed deep network adopts a conditional constraint to match the class conditional distributions across domains, where a great number of training samples from the source domain and a small number of training samples from the target domain are utilized to train the deep model. Cross-scene classification results on two HSIs demonstrate that the proposed network is able to yield superior performance compared with some related methods.