A method for oil spill detection and recognition in SAR remote sensing images

Abstract

The invention provides a SAR remote sensing image oil spill detection and recognition method, the specific process is as follows, using the Gamma MAP filter to filter the SAR image, and then performing Sobel filtering on it; performing a watershed algorithm on the gradient map obtained after the Sobel filtering to realize sea and land segmentation ;Use the mean value of the sea area image to fill the land area, and then use the C-V algorithm to segment and extract the target area in the homogeneous area of ​​the filled image; extract the gray level co-occurrence matrix of the target area, the texture characteristics of wavelet decomposition, gray The visual frequency histogram is constructed using the degree and shape features, and the trained SVM classifier model is used to classify the visual frequency histogram, and the suspected oil spill area is removed from the target area to realize the initial false alarm elimination; the result of the initial false alarm elimination As the initial label field, based on the initial label field, the feature field in the MRF context model is used to further eliminate false alarms, so as to realize the oil spill detection and recognition method of the SAR remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of image detection and recognition, and in particular relates to a SAR remote sensing image oil spill detection and recognition method. Background technique [0002] Image detection and recognition is a very widely used technology. In all fields involving image processing, it is ultimately necessary to perform image detection and recognition. It is an image processing system. It contains many aspects of image processing technology, just to finally achieve the purpose of detection and recognition. Therefore, image detection and recognition systems are often generally similar in process, and image preprocessing, image segmentation, target feature extraction, (target training and detection) classifier training, and target recognition and false alarm removal are required respectively. But for each system branch step, different methods can be used to achieve the expected image processing purpose. [0003] For t...

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Problems solved by technology

[0004] In order to solve the problem that the interference caused by multiplicative noise cannot accurately detect and identify the oil spill area in the image, the present invention proposes a relatively effective SAR image oil spill detection and identification method

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