Clustering-based feature selection for content based remote sensing image retrieval

Authors:
Shijin Li;Jiali Zhu;Jun Feng;Dingsheng Wan
Affiliations:
School of Computer & Information Engineering, Hohai University, Nanjing, China;School of Computer & Information Engineering, Hohai University, Nanjing, China;School of Computer & Information Engineering, Hohai University, Nanjing, China;School of Computer & Information Engineering, Hohai University, Nanjing, China
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Year:
2012

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Abstract

During the last three decades, the imaging satellite sensors have acquired huge quantities of remote sensing data. Content-based image retrieval is one of the effective and efficient techniques for utilizing those Earth observation data sources. In this paper, a novel remote sensing image retrieval approach, which is based on feature selection and semi-supervised learning, is proposed. The new method includes four steps. Firstly, clustering is employed to select features and the number of clusters is determined automatically by using the MDL criterion; Secondly, according to an improved clustering validity index, we select the optimal features which can describe the retrieval objectives efficiently; Thirdly, the weights of the selected features are dynamically determined; and finally, an appropriate semi-supervised learning scheme is adaptively selected and image retrieval is thus conducted. Experimental results show that, the proposed approach can achieve comparable performance to the relevance feedback method, while ours need no human interaction.