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Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

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Název: Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification
Autoři: Xiaoyang Zhao, Jian Zhang, Chenghai Yang, Huaibo Song, Yeyin Shi, Xingen Zhou, Dongyan Zhang, Guozhong Zhang
Zdroj: Remote Sensing, Vol 10, Iss 5, p 663 (2018)
Informace o vydavateli: MDPI AG
Rok vydání: 2018
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: optical multimodal images, registration, FAST, window selection, histogram specification, Science
Popis: In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method ...
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: http://www.mdpi.com/2072-4292/10/5/663; https://doaj.org/toc/2072-4292; https://doaj.org/article/22414d2c35ec4c3db5baf0dfa5b5c8a2
DOI: 10.3390/rs10050663
Dostupnost: https://doi.org/10.3390/rs10050663
https://doaj.org/article/22414d2c35ec4c3db5baf0dfa5b5c8a2
Přístupové číslo: edsbas.52F0ADE2
Databáze: BASE
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  – Url: https://doi.org/10.3390/rs10050663#
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xiaoyang+Zhao%22">Xiaoyang Zhao</searchLink><br /><searchLink fieldCode="AR" term="%22Jian+Zhang%22">Jian Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Chenghai+Yang%22">Chenghai Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Huaibo+Song%22">Huaibo Song</searchLink><br /><searchLink fieldCode="AR" term="%22Yeyin+Shi%22">Yeyin Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Xingen+Zhou%22">Xingen Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Dongyan+Zhang%22">Dongyan Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Guozhong+Zhang%22">Guozhong Zhang</searchLink>
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  Label: Source
  Group: Src
  Data: Remote Sensing, Vol 10, Iss 5, p 663 (2018)
– Name: Publisher
  Label: Publisher Information
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  Data: MDPI AG
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  Label: Publication Year
  Group: Date
  Data: 2018
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22optical+multimodal+images%22">optical multimodal images</searchLink><br /><searchLink fieldCode="DE" term="%22registration%22">registration</searchLink><br /><searchLink fieldCode="DE" term="%22FAST%22">FAST</searchLink><br /><searchLink fieldCode="DE" term="%22window+selection%22">window selection</searchLink><br /><searchLink fieldCode="DE" term="%22histogram+specification%22">histogram specification</searchLink><br /><searchLink fieldCode="DE" term="%22Science%22">Science</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method ...
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  Data: http://www.mdpi.com/2072-4292/10/5/663; https://doaj.org/toc/2072-4292; https://doaj.org/article/22414d2c35ec4c3db5baf0dfa5b5c8a2
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  Data: 10.3390/rs10050663
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  Data: https://doi.org/10.3390/rs10050663<br />https://doaj.org/article/22414d2c35ec4c3db5baf0dfa5b5c8a2
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        Value: 10.3390/rs10050663
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      – Text: English
    Subjects:
      – SubjectFull: optical multimodal images
        Type: general
      – SubjectFull: registration
        Type: general
      – SubjectFull: FAST
        Type: general
      – SubjectFull: window selection
        Type: general
      – SubjectFull: histogram specification
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      – SubjectFull: Science
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      – TitleFull: Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification
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