Oil Spill Detection by SAR Images Based on Human Perception

In the study of marine oil spill detection based on SAR images, identification efficiency and accuracy are the key issues. A human expert can determine the oil film,look-alikes, and sea surfaces more easily from texture features, so texture features are an important resource. On the one hand, our algorithm merges Tamura and GLCM features with the original SAR image, and extracts features from SAR image directly. This is an attractive solution for oil spill detection using small samples. This approach also avoids image segmentation, denoising preprocessing, and improves the feasibility and efficiency of identification recognition algorithms. On the other hand, we applied the deep belief network (DBN) to mimic the human perception system's efficient and accurate representation of information to get the essential features. We use the idea of human perception to classify the three kinds of samples (oil film, look-alikes and sea surface).Through experiments; we determined key parameter values in the DBN. Recognition accuracy of this algorithm on three kinds of samples of the original SAR image reached 90.36%, and it has good practical value.