Unsupervised iterative CEM-clustering based multiple Gaussian feature extraction for hyperspectral image classification
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Type of Work10 pages
conference papers and proceedings
Citation of Original PublicationBai Xue, Shengwei Zhong, Xiaodi Shang, Peter F. Hu, and Chein-I Chang "Unsupervised iterative CEM-clustering based multiple Gaussian feature extraction for hyperspectral image classification", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861M (14 May 2019); https://doi.org/10.1117/12.2519160
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hyperspectral image classification
multiple Gaussian features
Recently, many spectral-spatial hyperspectral image classification techniques have been developed, such as widely used EPF-based and composite kernel-based approaches. However, the performance of these types of spectral-spatial approaches are generally depends on both techniques and its guided spatial feature information. To address this issue, an unsupervised subpixel detection based hyperspectral feature extraction for classification approach is proposed in this paper. Harsany-Farrand-Chang (HFC) method is utilized to estimate the number of distinct features of hyperspectral image can be decomposed into, and simplex growing algorithm (SGA) is utilized to generate endmembers as initial condition for K-means clustering. Subpixel detection maps are generated by constrained energy minimization (CEM) using centroid of K-means clusters. To capture spatial information, multiple Gaussian feature maps are generated by applying Gaussian spatial filters with different σ on CEM detection maps, and PCA is used to reduce the dimension of multiple Gaussian feature maps, and feedback it into hyperspectral band images to reprocess K-means in an iteration manner. The proposed unsupervised approach is evaluated by supervised approaches such as iterative CEM (ICEM), EPF-based, and composite kernel-based methods, and results shows that most classification performance is improved.