Multiscale SAR Image Segmentation Using Support Vector Machines


A method for the segmentation of synthetic aperture radar (SAR) image is presented in this paper. The method integrates the use of multi-scale technology, mixed-model information and support vector machines (SVM). First, the multi-scale autoregressive (MAR) model is modeled for multi-scale sequence of SAR image, and a multi-scale features, which is used as input of SVM, are extracted via the MAR model. Then, SVM is trained and the SAR image is segmented with the trained SVM. So, this method not only can be fully taken advantage of the statistical information of SAR images in multi-scale sequence but also ability of SVM classifier. The experimental results show that the method has a very effective computational behavior and effectiveness, and decrease the time and increase the quality of SAR image segmentation.

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@article{Liu2008MultiscaleSI, title={Multiscale SAR Image Segmentation Using Support Vector Machines}, author={Ting Liu and Xian-Bin Wen and Jin-Juan Quan and Xue-Quan Xu}, journal={2008 Congress on Image and Signal Processing}, year={2008}, volume={3}, pages={706-709} }