Gravitation-Based Edge Detection in Hyperspectral Images
Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral...
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doaj-a6969859385143c9bc9adb78f045ca7b2020-11-24T21:01:40ZengMDPI AGRemote Sensing2072-42922017-06-019659210.3390/rs9060592rs9060592Gravitation-Based Edge Detection in Hyperspectral ImagesGenyun Sun0Aizhu Zhang1Jinchang Ren2Jingsheng Ma3Peng Wang4Yuanzhi Zhang5Xiuping Jia6School of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaSchool of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaDepartment of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKInstitute of Petroleum, Heriot-Watt University, Edinburgh EH14 4AS, UKSchool of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaKey Lab of Lunar Science and Deep-Exploration, Chinese Academy of Sciences, Beijing 100012, ChinaSchool of Engineering and Information Technology, The University of New South Wales at Canberra, Canberra ACT 2600, AustraliaEdge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitational theory, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of four state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method.http://www.mdpi.com/2072-4292/9/6/592edge detectionhyperspectral imagegravitationremote sensingfeature space |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Genyun Sun Aizhu Zhang Jinchang Ren Jingsheng Ma Peng Wang Yuanzhi Zhang Xiuping Jia |
spellingShingle |
Genyun Sun Aizhu Zhang Jinchang Ren Jingsheng Ma Peng Wang Yuanzhi Zhang Xiuping Jia Gravitation-Based Edge Detection in Hyperspectral Images Remote Sensing edge detection hyperspectral image gravitation remote sensing feature space |
author_facet |
Genyun Sun Aizhu Zhang Jinchang Ren Jingsheng Ma Peng Wang Yuanzhi Zhang Xiuping Jia |
author_sort |
Genyun Sun |
title |
Gravitation-Based Edge Detection in Hyperspectral Images |
title_short |
Gravitation-Based Edge Detection in Hyperspectral Images |
title_full |
Gravitation-Based Edge Detection in Hyperspectral Images |
title_fullStr |
Gravitation-Based Edge Detection in Hyperspectral Images |
title_full_unstemmed |
Gravitation-Based Edge Detection in Hyperspectral Images |
title_sort |
gravitation-based edge detection in hyperspectral images |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2017-06-01 |
description |
Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitational theory, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of four state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method. |
topic |
edge detection hyperspectral image gravitation remote sensing feature space |
url |
http://www.mdpi.com/2072-4292/9/6/592 |
work_keys_str_mv |
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1716777312445267968 |