Domain Adaptation With Preservation of Manifold Geometry for Hyperspectral Image Classification

Abstract

Adapting a pretrained classifier with unlabeled samples from an image for classification of another related image is a common domain adaptation strategy. However, traditional adaptation methods are not effective when the drift of spectral signatures is significant. Instead of iteratively redefining classifier parameters or decision boundaries, we exploit similar data geometries of images and preserve essential common data characteristics in a joint manifold space where similar samples are clustered. The proposed classification framework is based on aligning two global data manifolds with bridging pairs. In addition to global structures, we also consider the local scale by incorporating similar local clusters into the alignment process. In experiments with challenging temporal and spatially disjoint hyperspectral data sets, the proposed framework provides favorable classification results compared to two baseline methods, naive k-NN in both the original space and the manifold derived from pooled data. In comparisons with four state-of-the-art domain adaptation benchmark methods, the proposed method is demonstrated to be a competitive domain adaptation method, especially for the case when spectral changes between two data domains are significant. Results also provide insights related to the usefulness of incorporating global and local geometric characteristics of remote sensing data for domain adaptation studies.