Robust local feature descriptor for multisource remote sensing image registration

Distinctive and robust local feature description is crucial for remote sensing application, such as image matching and image retrieval. A descriptor for multisource remote sensing image matching that is robust to significant geometric and illumination differences is presented. In the proposed method, a traditional scale-invariant feature transform algorithm is applied for local feature extraction and a feature descriptor, named robust center-symmetric local-ternary-pattern (CSLTP) based self-similarity descriptor, is constructed for each extracted feature point. The main idea of the proposed descriptor is a rotation invariance description strategy on local correlation surface. Unlike common distribution-based descriptors or geometric-based spatial pooling descriptors, the proposed descriptor uses rotation invariance statistically strategic for CSLTP description on a correlation surface, which is inherently rotation invariant and robust to complex intensity differences. Then, a bilateral matching strategy followed by a reliable outlier removal procedure in the geometric transformation model is implemented for feature matching and mismatch elimination. The proposed method is successfully applied for matching various multisource satellite images and the results demonstrate its robustness and discriminability compared to common local feature descriptors.