MISR: Robust Multimodal Remote Image Matching of Earth and Planetary Using Modality-Invariant Structural Representation

Accurate image matching is a crucial prerequisite for multimodal data fusion in Earth observation and serves as a foundational technology for deep-space exploration. While Earth multimodal remote sensing images (EMRSIs) often suffer from severe nonlinear radiometric differences (NRD) and geometric deformations, planetary multimodal remote sensing images (PMRSIs) introduce additional challenges due to disparate sensing modalities and predominantly weak-texture surfaces. To address these cross-domain challenges, we propose a Modality-Invariant Structural Representation (MISR) framework designed for robust matching of both EMRSIs and PMRSIs. The MISR framework consists of two core modules. First, a novel image scale space is constructed using Weighted Side-window based Gradient Guided Filtering (WSGGF), which facilitates effective noise suppression while preserving essential structural and edge features. Second, a multi-order structural representation is introduced via the Multi-order Gradient Location and Orientation Histogram (MGLOH) descriptor, which integrates a weighted dominant orientation assignment with an enhanced spatial configuration. By fusing first- and second-order gradient responses, MGLOH offers improved rotation invariance and stronger feature discrimination. Extensive experiments conducted on both Earth and planetary datasets demonstrate that the proposed method achieves superior matching accuracy and robustness compared to state-of-the-art approaches.