Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors

One important task of site monitoring is change detection from aerial images. Change, in general, can be of various types. In this paper we address the problem of developmental change at a site. For instance, we would like to know about new construction at a previously undeveloped site and possibly monitor its progress. Model based approaches are not suited for this kind of change as it usually happens in unmodeled areas. Since it is difficult to infer construction activity by predicting and verifying specific local features, we rely on more global statistical indicators.The thesis of this paper is that the change induced by human activity can be inferred from changes in the organization among the visual features. Not only will the attributes of the individual image features change but also the relationships among these features will evolve. With the progress of construction we expect to see increased structure among the image features. We exploit this emerging structure, or organization, to infer change. In this paper, we propose four measures to quantify the global statistical properties of the individual features and the relationships among them. We base these measures on the theory of graph spectra. We provide extensive analysis of the robustness of these measures under various imaging conditions and demonstrate the ability of these organization-based measures to detect coarsely incremental developmental changes.