A modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, China;2. Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, 235 Jones Street, Ultimo, NSW 2007, Sydney, Australia;3. Computer School, Wuhan University, China;1. Quantum Computation & Intelligent Systems (QCIS) Centre, FEIT, University of Technology Sydney, NSW 2007, Australia;2. Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA |
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| Abstract: | In automated remote sensing based image analysis, it is important to consider the multiple features of a certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therefore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination coefficients for different features in the proposed multiple features DR optimization. Compared with conventional remote sensing image DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance. |
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| Keywords: | Hyperspectral image Multiple features Stochastic neighbor embedding Dimension reduction Classification |
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