NDLI: Linear unmixing of multidate hyperspectral imagery for crop yield estimation

Content Provider	IEEE Xplore Digital Library
Author	Bin Luo Chenghai Yang Chanussot, J.
Copyright Year	2011
Description	Author affiliation: Department of Image and Signal, GIPSA-Lab, Grenoble, France (Chanussot, J.) \|\| LIESMARS, Wuhan University, China (Bin Luo) \|\| US Department of Agriculture, Agricultural Research Service, Weslaco, Texas, USA (Chenghai Yang)
Abstract	In this paper, we have evaluated unsupervised unmixing approach (VCA) for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by both approaches are strongly correlated to the yield data (the correlation coefficients are between 0.7 to 0.8). The results validate the higher efficiency of the unsupervised unmixing approaches compared with the supervised methods presented [13] for the purpose of yield estimation. In addition, the unmixing was performed on the hyperspectral images of the same fields, but taken on two different dates. The results show that the correlations between the vegetation abundances and the yield image might change in terms of the observation dates. However, by combining the vegetation abundances extracted on different dates (by computing their products or the square roots of the products), the correlations are significantly improved so that we can always obtain the highest correlations.
Starting Page	1573
Ending Page	1576
File Size	511508
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049371
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Agriculture Vegetation mapping Correlation Soil Yield estimation
Content Type	Text
Resource Type	Article

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