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一种基于概率潜在语义模型的高分辨率遥感影像分类方法

陶超,谭毅华,彭碧发,田金文   

  1. 华中科技大学
  • 收稿日期:2009-12-28 修回日期:2010-08-24 出版日期:2011-04-25 发布日期:2011-04-25
  • 通讯作者: 陶超

A Probabilistic Latent Semantic Analysis Based Classification for High Resolution Remotely Sensed Imagery

  • Received:2009-12-28 Revised:2010-08-24 Online:2011-04-25 Published:2011-04-25

摘要: 高分辨率遥感影像中同谱异物,异物同谱现象对影像分类过程造成很大干扰,这与文本分析中同义词和多义词对文本语义理解的影响十分一致。针对这一问题,本文将文本分析中的概率潜在语义模型应用于高分辨率遥感影像分类,提出一种无监督的遥感影像分类新方法,分为三个阶段。第一阶段,利用均值移动分割方法对影像进行分割提取同质区域;第二阶段:提取分割后的各区域中每个像元的Gabor纹理特征,并对这些特征进行聚类形成视觉词汇;第三阶段:将分割后的各区域看成待分析文本集中的文本,将区域的类别看成文本主题,而区域所包含的视觉词汇即为文本中的单词,然后利用PLSA方法对同义词和多义词较强的鉴别能力对各区域进行分析,找出其最可能属于的主题或者类别,从而完成影像分类。实验结果表明,该方法能有效提高高分辨率遥感影像分类精度。

Abstract: The spectrum variation of high infraclass and low interclass in high-resolution remotely sensed imagery has seriously disturbed the process of imagery classification. This phenomenon is similar to the misunderstanding of document semantic information caused by synonym and antonym. To solve this problem, a new unsupervised classification algorithm for high spatial resolution remotely sensed imagery, which combines Gabor texture feature and PLSA model (Probabilistic Latent Semantic Analysis), is presented in this paper. Firstly, we extract homogeneous segments from original imagery through MeanShift segmentation. Secondly, Gabor texture features of every pixel in each region are extracted, and clustered into several visual words. Thus, in our case, the imagery segments correspond to the documents, the visual words used to describe the segments correspond to the words in the documents, and the categories to be discovered for each segment correspond to the topics of the documents. Finally, we use PLSA model to analyze each segment, and achieve the image classification by assigning the most likely category for them. The experimental results have shown that the approach can outperforms the existing algorithms in terms of classification accuracy.