Preprint Article Version 1 This version is not peer-reviewed

Gravitation-Based Edge Detection for Hyperspectral Images

Version 1 : Received: 18 May 2017 / Approved: 19 May 2017 / Online: 19 May 2017 (06:00:18 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Remote Sens. 2017, 9, 592
DOI: 10.3390/rs9060592

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 gravitation, 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 three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

Subject Areas

edge detection; hyperspectral image; gravitation; remote sensing; feature space

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