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A Sensing Image Segmentation Scheme Based on Support Vector Machine and LBP Model

Weichu Xiao, Weihong Chen


Sensing image has important geographical structure for detection and identification of the sense. In military and civilian, they have very important significances. It is proposed by support vector machine (SVM) and local binary pattern model in high-resolution remote sensing image of detection algorithm. Firstly, according to the target resolution remote sensing image features, sample image’s texture features and structure features have the reference points of information diffusion eigenvectors and sample-based training support vector machine classifiers to achieve goals coarse split. Then coarse segmentation results are the basis of the local binary pattern (LBP) model extraction precise contours. With one meter resolution satellite images experiment testing, the results are shown that the proposed algorithm has high accuracy and flexibility. It can be accurately detected by complex background target area with broad applicability.


Support Vector Machine, LBP Model, Benchmark Information Diffusion, Remote Sensing Image.

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