徐侃, 杨文, 陈丽君, 孙洪. 利用主题模型的遥感图像场景分类[J]. 武汉大学学报 ( 信息科学版), 2011, 36(5): 540-543.
引用本文: 徐侃, 杨文, 陈丽君, 孙洪. 利用主题模型的遥感图像场景分类[J]. 武汉大学学报 ( 信息科学版), 2011, 36(5): 540-543.
XU Kan, YANG Wen, CHEN Lijun, SUN Hong. Satellite Image Scene Categorization Based on Topic Models[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 540-543.
Citation: XU Kan, YANG Wen, CHEN Lijun, SUN Hong. Satellite Image Scene Categorization Based on Topic Models[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 540-543.

利用主题模型的遥感图像场景分类

Satellite Image Scene Categorization Based on Topic Models

  • 摘要: 提出了一种基于主题模型与特征组合相结合的遥感图像分类方法。该方法首先对图像进行尺度不变特征变换(SIFT)、几何模糊特征(GB)和颜色直方图特征(CH)提取,接着利用潜在概率语义分析(pLSA)模型分别对所得到的图像特征进行潜在主题的挖掘,然后对所得到的主题概率特征进行组合,最后利用支持向量机(SVM)分类器进行场景分类。实验表明,与传统分类方法相比,主题模型更具优势;与使用单特征相比,特征组合具有更高的分类准确率。

     

    Abstract: We present a scene classification method for satellite images based on the topic model-probabilistic latent semantic analysis(pLSA) and feature-combination.Firstly,three kinds of features(SIFT,geometric blur and colorhistogram) are extracted from images.Then,we apply the the pLSA model on these image features to obtain the probabilities of latent topics which will be combined subsequently.Finally,we implement SVM classification based on these probability features.The experimental results on the 12-category dataset show that our proposed method performs better in scene classification.

     

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