The Study Of BP Fusion Model For The Edge Detection Of Oil Spills On The Sea By Remote Sensing

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, such as Roberts operator, Sobel operator, algorithm of Marr edge detection and so on. These edge detection algorithms always have their own advantages and disadvantages for processing the different kinds of images. 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, owned the advantages of having less computation time and also got a better detection performance than these simple operators. The reason that 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 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 of oil spill image's edge detection.