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Proceedings Paper

BP fusion model for the detection of oil spills on the sea by remote sensing
Author(s): Weiwei Chen; Jubai An; Hande Zhang; Bin Lin
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Paper Abstract

Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills’ image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators’ result of edge gray level and the image’s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image’s edge detection.

Paper Details

Date Published: 16 June 2003
PDF: 11 pages
Proc. SPIE 4897, Multispectral and Hyperspectral Remote Sensing Instruments and Applications, (16 June 2003); doi: 10.1117/12.467327
Show Author Affiliations
Weiwei Chen, Dalian Maritime Univ. (China)
Jubai An, Dalian Maritime Univ. (China)
Hande Zhang, China Marine Supervision (China)
Bin Lin, Dalian Maritime Univ. (China)

Published in SPIE Proceedings Vol. 4897:
Multispectral and Hyperspectral Remote Sensing Instruments and Applications
Allen M. Larar; Qingxi Tong; Makoto Suzuki, Editor(s)

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