Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter

Yao, Yongxiang; Zhang, Yongjun; Wan, Yi; Liu, Xinyi; Yan, Xiaohu; Li, Jiayuan

doi:10.1109/tip.2022.3157450

Cited by 70 publications

(32 citation statements)

References 37 publications

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“…A complete description of the employed images is presented in Table V. We compare our proposal with the algorithms: Histogram of Phase Congruency (HOPC), Channel Features of Orientated Gradients (CFOG) [56] and Co-occurrence Filter Space Matching (CoFSM) [57]. All implementations used in this comparison are public and can be found at the links provided by the authors.…”

Section: Resultsmentioning

confidence: 99%

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Hugo¹,

Aranda²,

Mejia-Zuluaga³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

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Section: Resultsmentioning

confidence: 99%

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Hugo¹,

Aranda²,

Mejia-Zuluaga³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…The test suggests that SHKP is better than NMI, and ECA is better than DE. Another set of tests compared the proposed methodology against state-of-the-art methodologies in registration of multispectral images: HOPC, CFOG [ 101 ] and CoFSM [ 102 ]. The proposed methodology proved competitive, with similar or better results for most types of pairs of image specters.…”

Section: Articlesmentioning

confidence: 99%

Evolutionary Image Registration: A Review

Cocianu

Uscatu²,

Stan³

2023

Sensors

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Image registration is one of the most important image processing tools enabling recognition, classification, detection and other analysis tasks. Registration methods are used to solve a large variety of real-world problems, including remote sensing, computer vision, geophysics, medical image analysis, surveillance, and so on. In the last few years, nature-inspired algorithms and metaheuristics have been successfully used to address the image registration problem, becoming a solid alternative for direct optimization methods. The aim of this paper is to investigate and summarize a series of state-of-the-art works reporting evolutionary-based registration methods. The papers were selected using the PRISMA 2020 method. The reported algorithms are reviewed and compared in terms of evolutionary components, fitness function, image similarity measures and algorithm accuracy indexes used in the alignment process.

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“…The source of the test images is a public dataset [12,[21][22][23].According to the imaging type, it mainly includes visible and visible images, visible and infrared images, visible and depth map, visible and artificially produced rasterized map images, day and night images, seasonal change images, and SAR images and visible images, We select one or two pairs of images from each of these seven types and display them in ten groups a-j. They mainly include the problems of multimodal remote sensing images, such as intensity difference, spatial distortion, rotation, scale difference, and detail difference , noise, etc.…”

Section: Data and Evaluationmentioning

confidence: 99%

“…The data that support the findings of this study are available from [ [12,[21][22][23]] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [ [12,[21][22][23]].…”

Section: Data Availabilitymentioning

confidence: 99%

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

Li¹,

Li²,

jiao³

2022

Preprint

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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.

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Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter

Cited by 70 publications

References 37 publications

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Evolutionary Image Registration: A Review

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

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