Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive Coherence Window and Texture Information

In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms.

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