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Proceedings Paper

Spot detection from MODIS imagery using 2P-CFAR
Author(s): Xianwen Ding; Xiaofeng Li
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Paper Abstract

Oil spills are one of the major environmental concerns, especially in the coastal zones of the ocean. Satellite remote sensing imagery has proved to be a useful tool for monitoring oil spills in the marine environment. With its two daily acquisitions and the possibility to obtain near-real-time data free of charge, the Moderate Resolution Imaging Spectroradiometer (MODIS) shows interesting potential as such a cost-effective supplementary tool. Several researches on oil spill detection in MODIS imagery has been carried out for the past few years. Basically, oil spills were manually detected from MODIS imagery [1,2]. The disadvantage of the manual detection method is inefficient and subjective. Shi et al. proposed an oil spill detection method from MODIS imagery by using fuzzy cluster and texture feature extraction [3]. It works in an automatic manner and does not require any priori knowledge of occurrence or the spectral attributes of spills. But its efficiency in very near shore regions is limited. Chen and Zhao detected oil spills from the oil-water contrast ratio image by using a thresholding method [4].They found that the oil-water contrast ratio can be enhanced by replacing the original image with the ratio image of two different band ones in 400-800 nm. To obtain the oil-water contrast ratio image from the MODIS imagery, they selected the oil spill area and the background sea area and then calculated the mean radiance or emissivity value in those areas. By doing so, the automation and the accuracy of the method were reduced. Adamo et al. [5] and Kudryavtsev et al. [6] proposed physical methods for oil spill detection from MODIS imagery acquired in sunglint conditions. These two methods take imaging geometry into consideration and have the aid of other models or functions such as the Cox and Munk (1954) model [7],the CMOD4 model [8,9], the ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric model, and the transfer function, which increase the algorithm complexity and rely on some assumptions.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 98151E (14 December 2015); doi: 10.1117/12.2204947
Show Author Affiliations
Xianwen Ding, Shanghai Ocean Univ. (China)
Xiaofeng Li, Shanghai Ocean Univ. (China)

Published in SPIE Proceedings Vol. 9815:
MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Jianguo Liu; Hong Sun, Editor(s)

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