Segmentation method of multi-scale marine oil spill SAR image based on jseg and spectral clustering

Abstract

In view of the poor anti-noise performance of the traditional gray-scale feature spectrum clustering algorithm, and the multi-feature-spectral clustering segmentation method based on the gray-level co-occurrence matrix has limitations such as single scale, sensitivity to directionality, and difficulty in selecting the best feature combination. A multi-scale sea oil spill SAR image segmentation method based on JSEG and spectral clustering is proposed. First, the J-image multi-scale image sequence proposed by the JSEG algorithm is used to extract the scale, spectrum and texture features of the original image, and the spectral clustering feature matrix is ​​constructed. According to the canonical cut criterion, the Laplace matrix is ​​processed by the K-means clustering method. Finally, the fusion strategy based on the voting mechanism is used for decision-level fusion of the segmentation results at a single scale, so as to realize the multi-scale segmentation of SAR image oil spill. Compared with the traditional multi-feature-spectral clustering method, multiple sets of experimental results prove that the present invention has higher segmentation accuracy and stronger robustness.

Description

technical field [0001] The invention relates to a multi-scale sea surface oil spill SAR image segmentation method based on JSEG and spectral clustering, and belongs to the technical field of SAR image segmentation. Background technique [0002] Marine oil spill pollution has brought serious harm to the marine ecosystem and the environment and economic development of coastal cities, and is one of the main marine pollution. Synthetic Aperture Radar (SAR) has all-weather and all-weather monitoring capabilities and can effectively penetrate clouds, so it has been widely used in marine oil spill monitoring. Sea oil spills have the characteristics of ground radar backscattering, which can cause the attenuation of Bragg waves, thereby reducing the roughness of the sea surface, and appear as darker bands or spots in SAR images. At the same time, other factors such as rain area, biological oil spill, marine natural surface film, low wind speed area, etc. also have similar characteri...

AI Technical Summary

Problems solved by technology

On the other hand, among the multiple texture feature vectors extracted from the gray level co-occurrence matrix, there is still no unified standard on how to select the appropriate texture feature vector to construct the feature matrix, so as to ensure the segmentation accuracy and effectively control the computational complexity.

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