Information extraction from spatial big data faces challenges in data relevancy analysis and heterogeneous datamod- eling. When the interested targets are more than one, the relevant analysis is often compromised. In this paper, a one-class oriented approach for effective feature selection and classification of remote sensing images is proposed. Mutual information (MI) is used as the feature selection criterion to cope with a wide range of data types. Then a cluster space (CS) representation is applied to model mul- timodal data and classifies each target class in turn. Hyperspectral and LiDARdata sets were used in the experiments. The test results demonstrate the advantage in terms of classification accuracies by focusing on one class at a time as compared to considering all classes simultaneously in classification.
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