This document evaluates using unsupervised linear unmixing of multi-date hyperspectral imagery to estimate crop yields. Vertex component analysis is used to extract spectral endmembers from the imagery. Crop abundance maps derived from linear unmixing show strong correlations with actual crop yield data, and fusing results from images on different dates improves the correlation further.
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Luo-IGARSS2011-2385.ppt
1. LINEAR UNMIXING OF MULTIDATE HYPERSPECTRAL IMAGERY FOR CROP YIELD ESTIMATION Bin Luo 1 , Chenghai Yang 2 and Jocelyn Chanussot 3 1 LIESMARS, Wuhan University, Wuhan, China 2 U.S. Department of Agriculture, Weslaco, Texas, USA 3 Grenoble Institute of Technology, Grenoble, France IGARSS 2011; 24 – 29 July, 2011; Vancouver, Canada
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8. Grain Sorghum Yield Data Collection Ag Leader PF3000 Yield Monitor Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
9. Yield Data Crop yield images of the two fields. 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
10. Fusion of Multi-date Unmixing Results Flow chart of the fusion of the multi-date unmixing results 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
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12. Fusion of Multi-date Unmixing Results M 18 (k) of Field 1 M 29 (k) of Field 1 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
13. Fusion of Multi-date Unmixing Results M 18 (k) of Field 2 M 29 (k) of Field 2 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation
14. Fusion of Multi-date Unmixing Results Correlation coefficients between the yield data and the (combined) crop abundances of Field 1 Correlation coefficients between the yield data and the (combined) crop abundances of Field 2 Recall that 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation M 1 M 2 M 3 M 4 C(M i , Y) 0.739 0.748 0.780 0.764 M 1 M 2 M 3 M 4 C(M i , Y) 0.648 0.721 0.735 0.701
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Notas do Editor
The purposes of this work is to evaluate the unsupervised linear unmixing approaches for hs images for crop yield estimation To use the multidate data in order to improve the estimation results
The spectrum of a pixel of a hyperspectral image is considered as a linear mixture of the spectra of several endmembers In this paper we use VCA method to unmix hyperspectral images. It extracts the extremal points of the simplex formed by the date points as the spectra of endmembers.
For each field, the crop abundances are firstly estimated by VCA and NNLS from the hyperspectral images taken at different dates separately. The crop abundances of different dates are then combined by different ways, see the next page.
M1 is simply the crop abundances computed on the image of the date 18-May-2001 M2 is simply the crop abundances computed on the image of the date 29-May-2001 The crop yield estimation is evaluated by computing the correlation coefficients between the crop yield data and the (combined) crop abundances
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