Unsupervised domain adaptation based on deep adapted features alignment

Abstract

Due to the difficulty of acquiring labeled hyperspectral image (HSI), an unsupervised classification method known as deep adapted features alignment (DAFA) was proposed. First, deep adapted features from two domains are extracted by a convolutional deep adaptation network. Then, in order to align the distribution of two domains more accurately, the class-wise covariance and centroid alignment between deep adapted features from two domains are realized by matrix transformation and translation operation while maintaining the manifold structure of source features. Finally, source covariance and centroid aligned features and labels are used to train a base classifier, and target predictions are obtained by this classifier. Experiments conducted on several real HSI dataset pairs demonstrate the proposed DAFA method can effectively reduce distribution discrepancy between different HSIs and obtain good classification results.