Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity (2103.16871v1)

Abstract: Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation, and use the extended model to build HOPCncc. Then a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images, and has been tested with a variety of optical, LiDAR, SAR and map data. The results show that HOPCncc is robust against complex non-linear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration.

• The paper presents a novel HOPC descriptor and HOPC_ncc metric for robust registration of multimodal remote sensing images.
• It leverages structural similarity via phase congruency to overcome nonlinear radiometric differences among optical, LiDAR, and SAR data.
• Results demonstrate superior correct match ratios and fast template matching, indicating strong potential in real-time geospatial analysis.

Multimodal Remote Sensing Image Registration Utilizing Structural Similarity: An Expert Overview

The paper detailed in this analysis addresses a principal challenge in the domain of remote sensing: the automatic registration of multimodal remote sensing images. Such images—comprising data from disparate sources such as optical, LiDAR, and Synthetic Aperture Radar (SAR)—often exhibit nonlinear radiometric differences due to their varied modalities and acquisition conditions. Registering these images is crucial for effective Earth observation applications, including image fusion and change detection.

Technical Approach

The authors propose a novel feature descriptor termed the Histogram of Orientated Phase Congruency (HOPC). This descriptor leverages the structural properties inherent in images, departing from traditional intensity-based metrics which struggle with nonlinear radiometric variances. The HOPC descriptor extends the conventional phase congruency model by generating orientation representation. It captures phase congruency's amplitude and orientation, thus highlighting the geometric structural similarities between images. This approach is contrasted against prevalent descriptors like SIFT, which are sensitive to these radiometric discrepancies.

A key innovation introduced is the similarity metric HOPC_ncc, derived using the normalized correlation coefficient (NCC) of HOPC descriptors. This metric enhances the robustness of image registration by focusing on structural similarity rather than pixel intensity.

Numerical Results and Comparative Analysis

The authors provide a comprehensive evaluation of the HOPC_ncc descriptor against standard metrics such as NCC and mutual information (MI). Through rigorous experimentation on diverse multimodal datasets—ranging from visible-infrared to visible-SAR images—the proposed metric consistently outperforms its counterparts, particularly in high-resolution datasets with prominent structural features. For instance, HOPC_ncc demonstrates an impressive ability to maintain a high correct match ratio (CMR) even under significant non-linear radiometric changes, outperforming other metrics such as MI, which suffers due to entropy sensitivity.

Moreover, the HOPC_ncc exhibits computational efficiency through its fast template matching scheme, making it more feasible for practical applications compared to MI, which is computationally intensive due to joint histogram computations.

Implications and Future Directions in AI

This paper's implications extend to enhanced processing of multimodal remote sensing data, promising improved integration and analysis accuracy in applications requiring precise image registration. The incorporation of structural similarity metrics could be further explored in areas of computer vision beyond remote sensing, enhancing object recognition and scene understanding tasks where traditional intensity-based methods fall short.

The potential to adapt HOPC_ncc for scale and rotation invariance represents a promising trajectory for future research, addressing current limitations when images contain significant geometric transformations. Furthermore, leveraging dimension-reduction techniques to enhance computational efficiency could bolster the application of such sophisticated metrics in real-time remote sensing operations.

In summary, this paper provides a robust framework for multimodal image registration, emphasizing structural over radiometric similarity, thereby paving the way for more resilient algorithms in AI-driven geospatial analysis.