Abstract
Multifractals have proven to be a valuable tool in image analysis applications and remote sensing (RS). They can be used for image segmentation, texture analysis, and classification. This study explores their applicability to change detection. We present a new approach to image texture description based on the calculation of a spatialized Hölder exponent on multispectral satellite data. Hölder exponents are evaluated in the context of an unsupervised change detection algorithm, and the classification of change in various land cover classes. Obtained results show that the sensitivity of proposed methods varies from 69% up to 93% with a considerably low level of false alarms.
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