A multi-scale segmentation method of oil spills in SAR images based on JSEG and spectral clustering

作者：Wang Chao; Xu Li Zhong; Wang Xin; Huang Feng Chen

来源：International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7(1): 425-432.

DOI：10.14257/ijsip.2014.7.1.39

• Decision level fusion
• Gray level co-occurrence matrix
• JSEG
• Multiscale segmentation
• SAR
• Spectral clustering
• Spectral clustering methods
• Spills detection

Image segmentation is a key step of oil spills detection in SAR images. For the problem that the traditional multi-spectral clustering algorithm with the features extraction by GLCM (Gray-Level Co-occurrence Matrix) has such limitations as direction sensitivities and difficulties in selecting the best feature combination etc., this paper proposes a multi-scale segmentation method of oil spills in SAR images based on JSEG and spectral clustering. Multi-scale J-images are used to extract the multi-features and the Laplace matrix is clustered by the K-means method. Finally, a decision-level fusion strategy is used to fuse the segmentation results from different scales. Two sets of experiments show that, compared to the traditional spectral clustering methods based on the gray feature and multi-textual features, the proposed method has higher accuracy and stronger robustness.

• 出版日期2014
• 单位河海大学