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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (3) : 174-180     DOI: 10.6046/gtzyyg.2016.03.27
Identification of hyperspectral features for subalpine typical vegetation in the upper reaches of the Minjiang River
DAI Xiaoai1, JIA Hujun1, ZHANG Xiaoxue1, WU Fenfang2, GUO Shouheng1, YANG Wunian1, YANG Ye1
1. State Key Laboratory of Geo-spatial Information Technology, Ministry of Land and Resources/Institute of Remote Sensing and GIS, Chengdu University of Technology, Chengdu 610059, China;
2. Editorial Board of Geomatics and Information Science of Wuhan University, Wuhan 430072, China
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Spectral characteristics analysis is the basis of spectral feature classification and matching in hyperspectral image processing. In this paper, the authors selected five kinds of subalpine forest vegetation to measure their field spectra in the upper reaches of the Minjiang River, which include gramineae mottled bamboo, herbaceous fern,pilea notate, arbor china fir and shrubs palm. Through constructing the high spectral similarity measure index, five measuring methods, i.e., Euclidean distance(ED), spectral angle mapper(SAM), spectral information divergence(SID), spectral information divergence-spectral angle mapper(SID(TAN))and spectral distance based on Douglas-Peucker(SDDP), were used to analyze the relative capability for recognizing forest vegetation on the plateau. According to the results obtained, the spectral feature difference in the five kinds of forest vegetation mainly lies in peaks and troughs in the spectral curves; pilea notate has the highest relative spectral discriminatory probability in ED similarity measurement; mottled bamboo and fern have the highest relative spectral discriminatory probability in SID and SID(TAN); China fir has the highest relative spectral discriminatory probability in SDDP. SAM, SDDP, ED, SID(TAN)and SID of the relative spectral discriminatory entropy are 1.51, 1.59, 1.61, 2.16 and 2.18 respectively. The research results showed that the means reduced the amount of calculation for doing the similarity measurement which extracted the spectral feature vectors with the SFT, DPBSR and DABSR, DPSR. In order to ensure the condition of similar recognition capability, the means can greatly improve the retrieval efficiency of the program, and hence they are the fast and efficient hyperspectral feature matching and retrieval methods.

Keywords mask      remote sensing      cloud-cloud shadow      decision tree(DT)classifier      random forests(RF) classifier     
:  TP751.1  
Issue Date: 01 July 2016
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LU Fengling
GONG Zaiwu
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LU Fengling,GONG Zaiwu. Identification of hyperspectral features for subalpine typical vegetation in the upper reaches of the Minjiang River[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 174-180.
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