Gravitation-based edge detection for hyperspectral images
Sun, Genyun and Zhang, Aizhu and Ren, Jinchang and Ma, Jingsheng and Wang, Peng and Zhang, Yuanzhi and Jia, Xiuping (2017) Gravitation-based edge detection for hyperspectral images. Remote Sensing, 9. 592. ISSN 2072-4292 (https://doi.org/10.3390/rs9060592)
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Abstract
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.
ORCID iDs
Sun, Genyun, Zhang, Aizhu, Ren, Jinchang ORCID: https://orcid.org/0000-0001-6116-3194, Ma, Jingsheng, Wang, Peng, Zhang, Yuanzhi and Jia, Xiuping;-
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Item type: Article ID code: 60938 Dates: DateEvent11 June 2017Published8 June 2017AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 14 Jun 2017 11:45 Last modified: 23 Sep 2024 13:56 URI: https://strathprints.strath.ac.uk/id/eprint/60938