NDLI: Learning recovered pattern from region-dependent model for hyperspectral imagery

Content Provider	IEEE Xplore Digital Library
Author	Huiwu Luo Lina Yang Haoliang Yuan Yuan Yan Tang
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Univ. of Macau, Macau, China (Huiwu Luo; Lina Yang; Haoliang Yuan; Yuan Yan Tang)
Abstract	The Compressive-Projection Principle Component Analysis (CPPCA) technique which recovers hyperspectral image(HSI) data from random projection efficiently, has been proved to be significant in decreasing signal-sensing costs at the sender. Inspired by the fact that the spectral signature of the same ground cover is similar, and two pixels of the neighborhood are likely to belonging to the same ground cover, this paper proposed a novel region-dependent approach CPPCA to recover HSI data. Due to the fact that the region map is critical to our proposed algorithm, herewith we employ a robust supervised Bayesian approach (LORSAL-MLL segmentation) which explores both the spectral and spatial information in an intuitive interpretation with small size samples to segment hyperspectral image into different regions. The CPPCA reconstruction procedure is then employed to each region independently other than each partition individually. The effectiveness and practicability of proposed region-dependent CPPCA (RDCPPCA) reconstructed algorithm is illustrated by real hyperspectral image data set with several criteria measurement.
Starting Page	150
Ending Page	155
File Size	258931
Page Count	6
File Format	PDF
ISBN	9781467364690
DOI	10.1109/CYBConf.2013.6617463
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-13
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Hyperspectral image segmentation Compressive sensing Principle component analysis Vectors Partitioning algorithms Image reconstruction Hyperspectral image reconstruction Hyperspectral imaging Signal to noise ratio
Content Type	Text
Resource Type	Article

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