Robust Multimodal Remote Sensing Image Matching Using Edge Consistency Scale-Space and Significant Relative Response

Multimodal remote sensing images (MRSIs) often suffer from severe nonlinear radiation distortions (NRDs) and significant geometric distortions, making precise matching challenging. We developed a feature-based matching algorithm to address this issue using edge consistency scale-space (ECSS) and significant relative response. By designing an edge consistency filtering (ECF), we construct a scale space that preserves the structural information of MRSI at various scales, enhancing scale invariance. ECSS computes feature descriptors using multiorientation filtering techniques to construct significant relative responses. This approach not only resists NRD but also utilizes information from all directional filters to build descriptors with higher discriminative power compared to direct filter responses or the maximum index map (MIM). To ensure rotational invariance, ECSS employs a robust technique for estimating the primary orientation. To further optimize matching accuracy and increase the number of effective matching points, ECSS uses a coarse-to-fine matching strategy. This involves using preliminary matching results to estimate the affine transformation between images, which then guides a more refined secondary matching. We evaluated the performance of ECSS on five different MRSI datasets and compared the results with nine state-of-the-art matching methods: ReDFeat, MINIMA-LG, radiation-variation insensitive feature transformation (RIFT), MS-HLMO, SRIF, weighted structure saliency feature (WSSF), POS-GIFT, oriented filter-based matching (OFM), and GLS-MIFT. The experimental results demonstrate that ECSS excels in all performance metrics, particularly in terms of stability and matching accuracy when handling MRSI data with high NRD and complex geometric transformations.