Unsupervised Alignment of Image Manifolds with Centrality Measures

Abstract

The re-use of available labeled samples to classify newly acquired data is a hot topic in pattern analysis and machine learning. Classification algorithms developed with data from one domain cannot be directly used in another related domain, unless the data representation or the classifier have been adapted to the new data distribution. This is crucial in satellite/airborne image analysis: when confronted to domain shifts issued from changes in acquisition or illumination conditions, image classifiers tend to become inaccurate. In this paper, we introduce a method to align data manifolds that represent the same land cover classes, but have undergone spectral distortions. The proposed method relies on a semi-supervised manifold alignment technique and relaxes the requirement of labeled data in all domains by exploiting centrality measures over graphs to match the manifolds. Experiments on multispectral pixel classification at very high spatial resolution show the potential of the method.