文章基本信息

标题：An adaboost-based iterated MRF model with linear target prior for synthetic aperture radar image classification
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作者：Xin Su ; Chu He ; Xinping Deng 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 7B
页码：547-551
出版社：Copernicus Publications
摘要：A supervised classification method based on AdaBoost posterior probability and Markov Random Fields (MRF) model with Linear Targets Prior (LTP) is proposed in this paper. Firstly in contrast with most existing regions (superpixels) based models, this approach captures contiguous image regions called superpixels from ratio response maps of original images. Secondly, Adaboost classifier is employed to get likelihood probability for Markov Random Filed (MRF). Meanwhile, linear targets prior information (LTP) is introduced into MRF model combining with Potts prior model to engage better edges in classification results. Finally, iterative strategy in MRF model improves the performance of classification. Compared with traditional MRF model, the proposed approach has effective improvement in SAR images classification in the experiments of this paper
关键词：SAR; image classification; Linear Targets Prior; Ratio Response; MRF; AdaBoost