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首页> 外文会议>Conference on Multispectral and Hyperspectral Remote Sensing Instruments and Applications Oct 25-27, 2002 Hangzhou, China >BP fusion model for the detection of oil spills on the sea by remote sensing
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BP fusion model for the detection of oil spills on the sea by remote sensing

机译：BP融合模型用于遥感海上溢油检测

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摘要

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 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 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 of oil spill image's edge detection.
机译：在许多国家，漏油是非常严重的海洋污染。为了通过遥感器检测和识别出海上溢油，科学家必须对遥感图像进行研究。对于海上溢油的检测，边缘检测是图像处理中的重要技术。有许多用于图像处理的边缘检测算法。这些边缘检测算法在图像处理中总是有自己的优点和缺点。根据海上溢油图像边缘检测的主要要求，计算时间和检测精度，我们开发了一种融合模型。该模型采用BP神经网络融合简单算子的检测结果。之所以选择BP神经网络作为融合技术，是因为简单算子的边缘灰度结果与图像真实边缘灰度之间的关系是非线性的，而BP神经网络则很好地解决了非线性识别问题。因此，本文利用一些溢油图像训练了BP神经网络，然后将BP融合模型应用于其他溢油图像的边缘检测，取得了良好的效果。本文还将一些梯度算子和拉普拉斯算子的检测结果与BP融合模型的结果进行比较，以分析融合效果。最后指出该融合模型在溢油图像边缘检测中具有较高的精度和较高的速度。

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