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Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement

1
Ocean College, Zhejiang University, Zhoushan, Zhejiang 316021, China
2
College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China
3
Institute of Advanced Technology, Zhejiang University, Hangzhou, Zhejiang 310007, China
4
East China Sea Environmental Monitoring Center, Shanghai, Ministry of Natural Resources of the People’s Rrepublic of China, Beijing 310058, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1142; https://doi.org/10.3390/rs11091142
Received: 4 March 2019 / Revised: 1 May 2019 / Accepted: 7 May 2019 / Published: 13 May 2019
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Abstract

Automatic colorless floating hazardous and noxious substances (HNS) spill segmentation is an emerging research topic. Xylene is one of the priority HNSs since it poses a high risk of being involved in an HNS incident. This paper presents a novel algorithm for the target enhancement of xylene spills and their segmentation in ultraviolet (UV) images. To improve the contrast between targets and backgrounds (waves, sun reflections, and shadows), we developed a global background suppression (GBS) method to remove the irrelevant objects from the background, which is followed by an adaptive target enhancement (ATE) method to enhance the target. Based on the histogram information of the processed image, we designed an automatic algorithm to calculate the optimal number of clusters, which is usually manually determined in traditional cluster segmentation methods. In addition, necessary pre-segmentation processing and post-segmentation processing were adopted in order to improve the performance. Experimental results on our UV image datasets demonstrated that the proposed method can achieve good segmentation results for chemical spills from different backgrounds, especially for images with strong waves, uneven intensities, and low contrast. View Full-Text
Keywords: chemical pollution; image segmentation; UV image; target enhancement; image processing chemical pollution; image segmentation; UV image; target enhancement; image processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhan, S.; Wang, C.; Liu, S.; Xia, K.; Huang, H.; Li, X.; Liu, C.; Xu, R. Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement. Remote Sens. 2019, 11, 1142.

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