ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 395-398, 2012
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/395/2012/
doi:10.5194/isprsannals-I-3-395-2012
© Author(s) 2012. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
23 Jul 2012
A MODIFIED STOCHASTIC NEIGHBOR EMBEDDING FOR COMBINING MULTIPLE FEATURES FOR REMOTE SENSING IMAGE CLASSIFICATION
L. Zhang1, L. Zhang1, D. Tao2, and X. Huang1 1The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Sydney, NSW 2007, Australia
Keywords: Multiple Features, Dimensional Reduction, Classification Abstract. In remote sensing image interpretation, it is important to combine multiple features of a certain pixel in both spatial and spectral domains to improve the classification accuracy, such as spectral signature, morphological property, and shape feature. Therefore, it is essential to consider the complementary property of different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a multi-feature dimension reduction algorithm under a probabilistic framework, modified stochastic neighbor embedding (MSNE). For each feature, a probability distribution is constructed based on SNE, and then we alternatively solve SNE and learn the optimal combination coefficients for different features in optimization. Compared with conventional dimension reduction strategies, the suggested algorithm can considers spectral, morphological and shape features of a pixel to achieve a physically meaningful low-dimensional feature representation by automatically learn a combination coefficient for each feature adapted to its contribution to subsequent classification. In experimental section, classification results using hyperspectral remote sensing image (HSI) show that this modified stochastic neighbor embedding can effectively improve classification performance.
Conference paper (PDF, 923 KB)


Citation: Zhang, L., Zhang, L., Tao, D., and Huang, X.: A MODIFIED STOCHASTIC NEIGHBOR EMBEDDING FOR COMBINING MULTIPLE FEATURES FOR REMOTE SENSING IMAGE CLASSIFICATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-3, 395-398, doi:10.5194/isprsannals-I-3-395-2012, 2012.

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