The Multiscale Change Profile: A Statistical Similarity Measure for Change Detection in Multitemporal SAR Images

In this paper, we present a new similarity measure for automatic change detection in multitemporal SAR images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated using a cumulant-based series expansion which approximates the probability density functions in the neighborhood of each image pixel. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given allowing a simple computation which depends only on the 4 first statistical moments of the pixels inside the analysis window. The concept of multiscale change profile (MCP) is also introduced and its optimized implementation is presented. MCP yields change information on a wide range of scales and better characterizes the appropriate scale to be used for the detection. Two simple examples of application show that the MCP allows the design of change indicators which provide better results than a monoscale analysis.