Unsupervised Multitemporal Domain Adaptation With Source Labels Learning

Abstract

Multitemporal domain adaptation (DA) is very useful for solving the spectral drift problem between different images and is a basis step of multitemporal classification. However, for high-resolution images, they always have a few spectral bands. A few spectral bands are difficult to establish accurate alignment model. In order to achieving accurate multitemporal alignment on a few spectral bands’ high-resolution images, source label learning step is proposed in this letter and used to optimize traditional manifold alignment (MA). The core of this method is to improve the erroneous manifold structure by combining majority voting and weighting coefficients. Besides, this method is a universal step and can be used for optimizing all MA methods. Two groups of data sets captured by Chinese GF1 and GF2 satellites are used for performance evaluation. The experimental results demonstrate the effectiveness of our method and indicate our method significantly outperforms the traditional DA methods.