A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in Polar Domain

This paper addresses unsupervised change detection by proposing a proper framework for a formal definition and a theoretical study of the change vector analysis (CVA) technique. This framework, which is based on the representation of the CVA in polar coordinates, aims at: i) introducing a set of formal definitions in the polar domain (which are linked to the properties of the data) for a better general description (and thus understanding) of the information present in spectral change vectors; ii) analyzing from a theoretical point of view the distributions of changed and unchanged pixels in the polar domain (also according to possible simplifying assumptions); iii) driving the implementation of proper pre-processing procedures to be applied to multitemporal images on the basis of the results of the theoretical study on the distributions; and iv) defining a solid background for the development of advanced and accurate automatic change-detection algorithms in the polar domain. The findings derived from the theoretical analysis on the statistical models of classes have been validated on real multispectral and multitemporal remote sensing images according to both qualitative and quantitative analyses. The results obtained confirm the interest of the proposed framework and the validity of the related theoretical analysis.