Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring

REMOTE SENSING OF ENVIRONMENT

卷:114

Article

Shu, Yuanming¹  Li, Jonathan¹  Yousif, Hamad¹  Gomes, Gary¹ 

[1] Univ Waterloo, Fac Environm, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada

关键词: Oil spill;    Dark-spot detection;    Intensity threshold;    Spatial density threshold;    Density estimation;   

DOI  :  10.1016/j.rse.2010.04.009

来源: Elsevier

【 摘 要 】

Dark-spot detection is a critical and fundamental step in marine oil-spill detection and monitoring. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar (SAR) intensity imagery is presented The key to the approach is making use of a spatial density feature to differentiate between dark spots and the background. A detection window is passed through the entire SAR image. First, intensity threshold segmentation is applied to each window. Pixels with intensities below the threshold are regarded as potential dark-spot pixels while the others are potential background pixels. Second, the density of potential background pixels is estimated using kernel density estimation within each window Pixels with densities below a certain threshold are the real dark-spot pixels. Third, an area threshold and a contrast threshold are used to eliminate any remaining false targets. In the last step, the individual detection results are mosaicked to produce the final result. The proposed approach was tested on 60 RADARSAT-1 ScanSAR intensity Images which contain verified oil-spill anomalies. The same parameters were used in all tests For the overall dataset, the average of commission error, omission error, and average difference were 70%, 6.1%, and 04 pixels, respectively The average number of false alarms was 0 5 per unit image and the average computational time for a detection window was 1.2 s using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is fast, robust and effective. (C) 2010 Elsevier Inc. All rights reserved.

【 授权许可】

Free