Accurate hyperspectral and infrared satellite image registration method using structured topological constraints

Zheng, Jiangbin; Xu, Qizhi; Zhai, Bo; Wang, Yue

doi:10.1016/j.infrared.2019.103122

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…SURF features have been designed to determine mapping key points between two images on the assumption that corresponding key points have a similar gradient pattern around them [36]. The parameters of the detectSURFFeatures function were modified to obtain the highest possible number of matching points, given that a high number of points is required with high-resolution images [37]. We observed that the highest number of matching points were detected in the green band image (2089), followed by the NIR band image (1770) and the blue and red band images (1599 and 1670, respectively).…”

Section: Discussionmentioning

confidence: 99%

Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

et al. 2021

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Cucumber powdery mildew, which is caused by Podosphaera xanthii, is a major disease that has a significant economic impact in cucumber greenhouse production. It is necessary to develop a non-invasive fast detection system for that disease. Such a system will use multispectral imagery acquired at a close range with a camera attached to a mobile cart’s mechanic extension. This study evaluated three image registration methods applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a MicaSense® RedEdge camera. The detection of matching points was performed using Speeded-Up Robust Features (SURF), and outliers matching points were removed using the M-estimator Sample Consensus (MSAC) algorithm. Three geometric transformations (affine, similarity, and projective) were considered in the registration process. For each transformation, we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each image registration. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best image registration method corresponded to the affine transformation because the RMSE is less than 1 pixel and the RMSEs have a Gaussian distribution for all of the bands, but the blue band.

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Section: Discussionmentioning

confidence: 99%

Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

et al. 2021

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“…LP-RANSAC [29] integrates local preserving constraint into the universal RANSAC framework, which prunes those unreliable correspondences before the hypothesize-andverify loop. Zheng [30] proposes an image registration method by using structured topological constraints, which establishes the correspondences of local points, triangular edges and triangular surfaces to dynamically erase mismatches. Recently, a semantic filter based on faster R-CNN is proposed to integrate with RANSAC [31], and three bad labels denoting low-quality feature correspondences are filtered out during the pre-processing phase.…”

Section: Related Workmentioning

confidence: 99%

“…We define the quality of each sample subset as follows. q Δ =(q(x 1 , y 1 )+q(x 2 , y 2 )+q(x 3 , y 3 ))/3×q Δarea ×P RSΔ (30) Equation (30) indicates that for a sample subset, the higher the quality of three points is, the larger the area of the triangle is, and the more consistent the area ratio of the matching triangle pairs is, the higher the quality of the sample subset is.…”

Section: B3 Sample Checkmentioning

confidence: 99%

Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

et al. 2021

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Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in longdistance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This paper puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the reprojection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.

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A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

Sheng

Liu

et al. 2023

International Journal of Remote Sensing

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Accurate hyperspectral and infrared satellite image registration method using structured topological constraints

Cited by 4 publications

References 25 publications

Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

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