Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity

By Yuanxin Ye, Jie Shan, Lorenzo Bruzzone, Li Shen. IEEE Transactions on Geoscience and Remote Sensing, 2017

447 citations recorded by the source corpus.

Automatic registration of multimodal remote sensing data [e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the significant nonlinear 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 nonlinear 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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/19e35d2ed08ffeba3cf4944021b98b093c7c427a

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Citations in the corpus

Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “Ye et al. (2017) proposed a novel feature descriptor based on the structural properties of images for multi-modal registration.”
RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform (2019, background): “…local self-similarity descriptor (LSS) [37], partial intensity invariant feature descriptor (PIIFD) [38], distinctive order-based self-similarity descriptor (DOBSS) [39], ARRSI [40], histogram of orien-tated phase congruency (HOPC) [41], and phase congruency structural descriptor (PCSD) [42].”
Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, methodology): “More-over, some state-of-the-art similarity measures, such as MI, GMI [43], HOG ncc , HOPC ncc [34], and modality independent neighborhood descriptor (MIND) [37], are used for comparisons.”
A review of multimodal image matching: Methods and applications (2021, methodology): “[421] integrated PC with an orientation histogram strategy to describe extracted features based on the structural properties of images; this process is called HOPC.”