A fast marine sewage detection method for remote-sensing image
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A fast marine sewage detection method for remote-sensing image Guoqiang Huan1,2 · Zhanjie Song1 · Shuo Zhang1,3 · Jianhua Zhu4
Received: 7 August 2017 / Accepted: 3 January 2018 © SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2018
Abstract This paper presents an effective method for marine sewage detection from a remote-sensing image. It is inspired by the Grab-Cut mechanism that iterative estimation and incomplete labeling allow a considerably reduced degree of user interaction for a given quality of result. By establishing the relationship between the color feature and the object seeds, we first model object and background with Gaussian mixture model, respectively, followed by iteratively updating the parameter of model to decline the energy function. To improve the computation efficiency, we propose to extend the region of interest as background. The proposed method accounts for not only the effect of color feature, but also the geographical information. The experimental results demonstrate that the proposed method is more reliable in marine sewage detection compared to other state-of-the-art methods. Keywords Image detection systems · Remote sensing and sensors · Digital image processing · Gaussian mixture model · Grab-Cut mechanism Mathematics Subject Classification 62H25 · 60J20 · 68Q87
Communicated by Cristina Turner.
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Shuo Zhang [email protected]
1
School of Mathematics, Tianjin University, Tianjin, China
2
Wuhan Space Sanjiang Laser Industry Technology Research Institute Co., Ltd., Wuhan, China
3
Department of Mathematics, Tianjin University of Finance and Economics, Tianjin, China
4
National Ocean Technology Center, Tianjin, China
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G. Huan et al.
1 Introduction The progress of human civilization and a comfortable lifestyle are accompanied with the pollution of the air, soil, and seas. Fortunately, the use of remote-sensing satellites allows us to track the pollution timely. Image segmentation is dividing the image into a number of procedures with consistency and non-overlapping regions. The topic of the interactive image segmentation has received considerable attention in the computer vision community in the last decades (Kolmogorov and Zabin 2004; Boykov et al. 2010; Boykov and Funka-Lea 2006). This paper is focused on how to detect the pollution region in remote-sensing (RS) images efficiently with interaction. The aim is to achieve high performance with the modest interactive effort for users. In general, the degrees of interactive effort range from editing individual pixels, at the labor-intensive extreme, to merely touching foreground and/or background in a few locations.
2 A brief review of interactive image segmentation In the following, we categorize different methods of interactive image segmentation by their methodology and user interfaces, mainly including segmentation in discrete domain and segmentation in continuous domain. The known appearance models typically are assumed in discrete domain of segmentation methods. The log-likelihoods of appeara
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