Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/94569
標題: Hyperspectral Image Classification Using Fast and Adaptive Bidimensional Empirical Mode Decomposition With Minimum Noise Fraction
作者: Ming-Der Yang
Kai-Shiang Huang
Yeh-Fen Yang
Liang-You Lu
Zheng-Yi Feng
Hui Ping Tsai
蔡慧萍
Project: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 13, NO. 12, DECEMBER 2016
摘要: 
The scattered pixel problem in hyperspectral images caused by atmospheric noises and incomplete classification can lead to unsatisfactory classification; this problem remains to be solved. This letter reports the application of minimum noise fractions (MNFs) combined with fast and adaptive bidimensional empirical mode decomposition (FABEMD) as a two-step process to improve the classification accuracy of airborne visible–infrared imaging spectrometer hyperspectral image of the Indian Pine data set. With dimensional reduction by using MNF, FABEMD, considered as a low-pass filter, decomposes a hyperspectral image into several bidimensional intrinsic mode functions (BIMFs) and a residue image. The first four BIMFs are removed and the remainder BIMFs are integrated to reconstruct informative images that are subsequently classified through a support vector machine classifier (SVM). The classification results show that the proposed approach can effectively eliminate noise effects and can obtain higher accuracy than does traditional MNF SVM.
URI: http://hdl.handle.net/11455/94569
DOI: 10.1109/LGRS.2016.2618930
Appears in Collections:土木工程學系所

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