In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images
for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic
Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant
of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling
(FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction
and shape detail preservation. MSERs of each scale space image are found and then combined through a
decision level fusion strategy, namely “selective scale fusion” (SSF), where contrast and boundary curvature of
each MSER are considered. The performance of the proposed method is evaluated using real multitemporal
high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with
several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change.
One of the main outcomes of this approach is that different objects having different sizes and levels of contrast
with their surroundings appear as stable regions at different scale space images thus the fusion of results from
scale space images yields a good overall performance.