DETECTION OF SPECIFIC CHANGES IN IMAGE TIME SERIES BY AN ADAPTIVE CHANGE VECTOR ANALYSIS

This paper presents an adaptive framework for detection of changes of relevance occurring in image time series in a recursive way. With the availability of reference data for only one image pair from the time series (source domain), the proposed methodology employs change vector analysis in the 3-dimensional spherical domain to determine a decision region R associated with the change of relevance. Then, by exploiting the similarity among domains, the same kind of change can be detected by adapting R to the rest of image pairs belonging to the time series. The methodology was tested in a multispectral time series made up by TMLandsat images marked by sequential deforestation activities in the Amazon with reference data. The quantitative analysis of the results indicates the soundness of the proposed approach.