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《Acta Geodaetica et Cartographica Sinica》 2012-02
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Unmixing of Hyperspectral Mixture Pixels Based on Spectral Multiscale Segemented Features

WU Bo1,XIONG Zhuguo2 1.Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education,Fuzhou University,Fuzhou 350002,China;2.School of Geomatics,East China Institute of Technology,Fuzhou 344000,China  
A novel approach to improve abundance estimation of hyper-spectral image using spectral piecewise constant features is presented.Firstly it extracts the spectral features by partitioning the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error.Then the multi-scale features can be determined by using optimal segmented scale or combination of features with different segmentation scales.In the end,the estimation is performed by unmixing the pixel in the feature space with constrained least square algorithm to achieve the respective abundance fractions of these end-members present in the pixel.Algorithm validation and comparison were done with simulated and real data.Experimental results demonstrate the proposed method can significantly improve the least squares estimation of end-member abundances using remotely sensed hyper-spectral signals,as compared to those of original hyper-spectral signals or discrete wavelet transform based features.
【Fund】: 国家自然科学基金(40801181;40901221);; 福建省自然科学基金(2010J01251);; 福建省科技重点项目(2011Y0036)
【CateGory Index】: P237
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