期刊
JOURNAL OF APPLIED REMOTE SENSING
卷 8, 期 -, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JRS.8.084691
关键词
N-FINDR; orthogonal subspace projection; spectral unmixing algorithm; fully constrained least squares
资金
- Chinese State Key Basic Research Project [2013CBA01802]
- Chinese Academy of Sciences Action Plan for West Development Project [KZCX2-XB3-15]
- National Natural Science Foundation of China [41101337, 31372367, 31228021]
- Fundamental Research Funds for the Central Universities [lzujbky-2013-103]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT13019]
We describe and validate an improved endmember extraction method to improve the fractional snow-cover mapping based on the algorithm for fast autonomous spectral endmember determination (N-FINDR) maximizing volume iteration algorithm and orthogonal subspace projection theory. A spectral library time series is first established by choosing the expected spectra information using prior knowledge, and the fractional snow cover (FSC) is then retrieved by a fully constrained least squares linear spectral mixture analysis. The retrieved fractional snow-cover products are validated by the FSC derived from Landsat imagery. Our results indicate that the improved algorithm can obtain the endmember information accurately, and the retrieved FSC has better accuracy than the MODIS standard fractional snow-cover product (MOD10A1). (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
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