<title>Unsupervised learning of spatial regularities</title>

Abstract

This paper examines the task of remote-sensing image analysis as an unsupervised learning task. Images are usually (very) large, and represent complex objects. Unsupervised learning, or clustering, may be of great help at several phases of the analysis. First, this paper describes a clustering algorithm. Then, the application of this algorithm to the segmentation phase is demonstrated. It is then argued that radiometry is insufficient to fully understand the scene in thematic terms. The next level of complexity is related to the incorporation of spatial information. This paper shows how this kind of data can be expressed. Clustering is then extended to deal with such complex, structured data. Experiments are provided to assess the validity of the approach. The set of experiments proves that clustering is a fundamental tool of remote-sensing image analysis, and that its scope may well be larger than was initially expected.