LAUSR

Automatic matching of synthetic aperture radar (SAR) and optical images is a fundamental task in many remote sensing applications. However, due to different imaging modalities, conventional matching methods provide limited performances. In this letter, based on the observation that structural features are maintained across different modality images, we propose a novel feature-based method to effectively address SAR and optical image matching. The proposed method is built on the phase congruency (PC) model and consists mainly of two stages. First, a modified version of the uniform nonlinear diffusion-based Harris (MUND-Harris) detector is introduced to extract the local features. Unlike the UND-Harris, MUND-Harris employs the PC instead of image intensity for feature extraction and thus obtain well-distributed and highly repeatable feature points. Second, a local structural descriptor, namely PC order-based local structural (PCOLS), is designed for the extracted points. PCOLS is constructed in a grouping manner and further encodes image structures with an adaptive descriptor structure, which provides robustness against modality variations including significant geometric and intensity differences. Experimental results obtained on several SAR and optical image pairs demonstrate the encouraging performance of the proposed method.