Clustering-Based Feature Selection for Content Based Remote Sensing Image Retrieval

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.