Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery

doi:10.1117/1.JRS.11.015006

CORC > 自然资源部第一海洋研究所 > 自然资源部第一海洋研究所 > 业务部门 > 海洋物理与遥感研究室

	Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery
	Lang, Haitao 1,2; Zhang, Xingyao 1; Xi, Yuyang 1; Zhang, Xi 3; Li, Wei 4
刊名	JOURNAL OF APPLIED REMOTE SENSING
	2017-01-12
卷号	11 期号:1
关键词	oil spill surveillance synthetic aperture radar remote sensing marine pollution
ISSN号	1931-3195
DOI	10.1117/1.JRS.11.015006
英文摘要	In recent years, oil spill surveillance with space-borne synthetic aperture radar (SAR) has received unprecedented attention and has been gradually developed into a common technique for maritime environment protection. A typical SAR-based oil spill detection process consists of three steps: (1) dark-spot segmentation, (2) feature extraction, and (3) oil spill and look-alike discrimination. As a preliminary task in the oil spill detection process chain, dark-spot segmentation is a critical and fundamental step prior to feature extraction and classification, since its output has a direct impact on the two subsequent stages. The balance between the detection probability and false alarm probability has a vital impact on the performance of the entire detection system. Unfortunately, this problem has not drawn as much attention as the other two stages. A specific effort has been placed on dark-spot segmentation in single-pol SAR imagery. A combination of fine designed features, including gray features, geometric features, and textural features, is proposed to characterize the oil spill and seawater for improving the performance of dark-spot segmentation. In the proposed process chain, a histogram stretching transform is incorporated before the gray feature extraction to enhance the contrast between possible oil spills and water. A simple but effective multiple-level thresholding algorithm is developed to conduct a binary classification before the geometric feature extraction to obtain more accurate area features. A local binary pattern code is computed and assigned as the textural feature for a pixel to characterize the physical difference between oil spills and water. The experimental result confirms that the proposed fine designed feature combination outperforms existing approaches in both aspects of overall segmentation accuracy and the capability to balance detection probability and false alarm probability. It is a promising alternative that can be incorporated into existing oil spill detection systems to further improve system performance. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
电子版国际标准刊号	19313195
资助项目	Higher Education and High-Quality and World-Class Universities[PY201619]
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
WOS记录号	WOS:000397642600003
内容类型	期刊论文
源URL	[http://ir.fio.com.cn/handle/2SI8HI0U/3249]
专题	业务部门_海洋物理与遥感研究室
作者单位	1.Beijing Univ Chem Technol, Dept Phys & Elect, 15 Beisanhuan East Rd, Beijing 100029, Peoples R China; 2.Beijing Univ Chem Technol, Beijing Key Lab Environm Harmful Chem Anal, 15 Beisanhuan East Rd, Beijing 100029, Peoples R China; 3.First Inst Oceanog SOA, Lab Remote Sensing, 6 Xianxialing Rd, Qingdao 266061, Peoples R China; 4.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Lang, Haitao,Zhang, Xingyao,Xi, Yuyang,et al. Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery[J]. JOURNAL OF APPLIED REMOTE SENSING,2017,11(1).
APA	Lang, Haitao,Zhang, Xingyao,Xi, Yuyang,Zhang, Xi,&Li, Wei.(2017).Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery.JOURNAL OF APPLIED REMOTE SENSING,11(1).
MLA	Lang, Haitao,et al."Dark-spot segmentation for oil spill detection based on multifeature fusion classification in single-pol synthetic aperture radar imagery".JOURNAL OF APPLIED REMOTE SENSING 11.1(2017).