An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model

Misra, Indranil; Moorthi, S. Manthira; Dhar, Debajyoti; Ramakrishnan, R.

doi:10.1109/rait.2012.6194482

Cited by 49 publications

(12 citation statements)

References 7 publications

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“…Hence, least square fitting cannot be directly used to post-process the initial road centerline network. In order to solve the problem, the Harris corner detection approach [43,55] is used to decompose the initial road centerline network into the road centerline segment. The approach recognizes corner points according to the average gray change, which is calculated by moving a local windows in different directions and can be expressed as follows:…”

Section: Road Centerline Extractionmentioning

confidence: 99%

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Zhang

Sun

et al. 2018

Remote Sensing

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The road networks provide key information for a broad range of applications such as urban planning, urban management, and navigation. The fast-developing technology of remote sensing that acquires high-resolution observational data of the land surface offers opportunities for automatic extraction of road networks. However, the road networks extracted from remote sensing images are likely affected by shadows and trees, making the road map irregular and inaccurate. This research aims to improve the extraction of road centerlines using both very-high-resolution (VHR) aerial images and light detection and ranging (LiDAR) by accounting for road connectivity. The proposed method first applies the fractal net evolution approach (FNEA) to segment remote sensing images into image objects and then classifies image objects using the machine learning classifier, random forest. A post-processing approach based on the minimum area bounding rectangle (MABR) is proposed and a structure feature index is adopted to obtain the complete road networks. Finally, a multistep approach, that is, morphology thinning, Harris corner detection, and least square fitting (MHL) approach, is designed to accurately extract the road centerlines from the complex road networks. The proposed method is applied to three datasets, including the New York dataset obtained from the object identification dataset, the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D semantic labelling benchmark and Guangzhou dataset. Compared with two state-of-the-art methods, the proposed method can obtain the highest completeness, correctness, and quality for the three datasets. The experiment results show that the proposed method is an efficient solution for extracting road centerlines in complex scenes from VHR aerial images and light detection and ranging (LiDAR) data.

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Section: Road Centerline Extractionmentioning

confidence: 99%

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Zhang

Sun

et al. 2018

Remote Sensing

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“…Invariance to scale is advantageous to only an extent and becomes a stone in the shoe, in case of large scale variations. The disadvantage of this detector is it is not invariant to large scale change [8]. Harris detector reveals the Ljunctions and points with the higher curvature along with the corner points for which second moment matrix and corner function is computed [5,6] .If both of the Eigen values of the second moment matrix are approximately equal and have high value then the taken point is detected as the corner point.…”

Section: Corner Detectionmentioning

confidence: 99%

Performance Analysis of Edges, Corners and the genres: A Subjective Estimation

Thareja¹

2016

IOSR

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“…The Harris corner is a representative point feature due to its illumination insensitivity and rotation invariance. The corner feature is applied in remote sensing image registration [ 29 , 30 ]. Pei et al [ 31 ] improved the Harris corner to solve the corner clustering problem and accelerate the registration speed.…”

Section: Introductionmentioning

confidence: 99%

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

Han

Liu

et al. 2021

Sensors

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It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.

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An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model

Cited by 49 publications

References 7 publications

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

Performance Analysis of Edges, Corners and the genres: A Subjective Estimation

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

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