Research On The Application In The Edge Detection Of Oil Spills By Remote Sensing Based On PCNN

Our country has paid much attention to the oil spills.We have the planes equipmented with different remote-sensing detection equipments that used to the oil spill detection.But the software for oil spill information has not integrated yet.So,it is key technology for oil spill to be detected more quickly and accurately.As the edge detection is an important technology in image processing, it plays an important role in discerning the existence of oil spills and estimating the magnitude and area of oil spills. Aim at the oil spills that have features like low contrast,noise pollution and relatively short computation time,we apply the new generation artificial neural networks----Pulse Coupled Neural Networks (PCNN) to oil spill detection. Compare to the traditional artificial neural network, PCNN has biological characteristics, it does not need accurate training and has good characteristics of pulse emission,parameter controllability.So it is widely used in image processing and pattern identification.The paper used PCNN to detect the grey of oil spill image.To the images with no noise pollution,we used the method of image segmentation to divide the image into different area and then marked the edge of gray images.To the images with noise pollution,we used PCNN to smooth and enhance images firstly,and then used PCNN to edge detection.At last ,the paper compared the images that processed by PCNN with common edge detection algorithms,such as Roberts operator,Sobel operator and so on. From the results, it pointed out that the PCNN model showed expected results in processing the oil spill images.