Multimodal Remote Sensing Image Registration Based on Adaptive Multi-scale PIIFD

TL;DR

This paper introduces a novel adaptive multi-scale PIIFD method for registering multimodal remote sensing images, effectively handling multiple modalities with improved accuracy and robustness over existing methods.

Contribution

The paper presents a new multimodal image registration approach based on adaptive multi-scale PIIFD, capable of registering multiple modalities simultaneously, unlike previous methods limited to two modalities.

Findings

Achieves superior registration accuracy compared to existing methods.

Effectively filters noise while retaining edge features using KAZE features.

Demonstrates robustness across various multimodal remote sensing datasets.

Abstract

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop, and the registration problem based on multimodal images has gradually emerged. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multiple modal image data. Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD(AM-PIIFD). This method extracts KAZE features, which can effectively retain edge feature information while filtering noise. Then adaptive multi-scale PIIFD is calculated for matching. Finally, the mismatch is removed through the consistency of the feature main direction, and the image alignment transformation is realized. The qualitative and quantitative comparisons with other three advanced methods shows that our method can achieve excellent performance in multimodal remote sensing image registration.