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Gravitation-Based Edge Detection in Hyperspectral Images

School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Institute of Petroleum, Heriot-Watt University, Edinburgh EH14 4AS, UK
Key Lab of Lunar Science and Deep-Exploration, Chinese Academy of Sciences, Beijing 100012, China
School of Engineering and Information Technology, The University of New South Wales at Canberra, Canberra ACT 2600, Australia
Author to whom correspondence should be addressed.
Academic Editors: Eyal Ben-Dor and Prasad S. Thenkabail
Remote Sens. 2017, 9(6), 592;
Received: 28 March 2017 / Revised: 18 May 2017 / Accepted: 8 June 2017 / Published: 11 June 2017
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. View Full-Text
Keywords: edge detection; hyperspectral image; gravitation; remote sensing; feature space edge detection; hyperspectral image; gravitation; remote sensing; feature space
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MDPI and ACS Style

Sun, G.; Zhang, A.; Ren, J.; Ma, J.; Wang, P.; Zhang, Y.; Jia, X. Gravitation-Based Edge Detection in Hyperspectral Images. Remote Sens. 2017, 9, 592.

AMA Style

Sun G, Zhang A, Ren J, Ma J, Wang P, Zhang Y, Jia X. Gravitation-Based Edge Detection in Hyperspectral Images. Remote Sensing. 2017; 9(6):592.

Chicago/Turabian Style

Sun, Genyun, Aizhu Zhang, Jinchang Ren, Jingsheng Ma, Peng Wang, Yuanzhi Zhang, and Xiuping Jia. 2017. "Gravitation-Based Edge Detection in Hyperspectral Images" Remote Sensing 9, no. 6: 592.

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