Feature-based multimodal remote sensing image matching: Benchmark and state-of-the-art

Abstract

Multimodal remote sensing image matching (MRSIM) is a crucial prerequisite in the remote sensing field, aiming to align images captured by different sensors to facilitate subsequent interpretation and analysis. In recent years, numerous efforts have been made to achieve feature-based MRSIM. However, there is a lack of a comprehensive review of advanced feature-based MRSIM methods and a comparison of their performance on diverse datasets. Additionally, existing datasets often have some limitations in terms of modality diversity and ground truth completeness, which prevent the validation of the performance of algorithms. This paper first provides an extensive overview of latest advances based on the general framework of feature-based MRSIM methods. Then, we summarize existing MRSIM datasets, and construct the HNU-DATASET, including four types of common MRSIM pairs and ground-truth annotations of each image pair. Finally, to ensure a comprehensive evaluation, several representative open-source methods, such as radiation-variation insensitive feature transform (RIFT) and histogram of absolute phase consistency gradients (HAPCG), are employed to benchmark performance on both the proposed HNU-DATASET and multiple publicly available datasets. The experimental results can serve as a valuable reference for future research, which can promote the development of advanced multimodal remote sensing.