Cross-Data Set Hyperspectral Image Classification Based on Deep Domain Adaptation

For hyperspectral image classification, there is a large gap between the theoretical method and the practical application. Hyperspectral image classification in theoretical research trains a new classifier for each data set, which is ineffective and even infeasible in large-scale applications. In this paper, we make a preliminary attempt to recycle the classification model to new data sets in an unsupervised way. Specially, we propose a cross-data set hyperspectral image classification method based on deep domain adaptation. The proposed method contains three modules: domain alignment module that learns to minimize the domain discrepancy with the guide of an irrelevant task, task allocation module that learns to classify on the source domain with the regulation of domain alignment, and domain adaptation module that transfers both the alignment ability and classification ability to the target domain by an adaptation strategy. As a result, with the information of an irrelevant task on dual-domain data sets, we can minimize the domain discrepancy and transfer the task-relevant knowledge from the source domain to the target domain in an unsupervised way. Extensive experiments on three hyperspectral images demonstrate the effectiveness of our method compared with other related methods when dealing with new data sets.