Luo-IGARSS2011-2385.ppt

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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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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

Mapping Yield Variation for Precision Agriculture ,[object Object],[object Object],[object Object],[object Object]

Spectral Mixing ,[object Object],[object Object],[object Object],[object Object],Plant Soil Mixture

Objectives and Procedures ,[object Object],[object Object],26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation VCA ( Vertex Component Analysis

[object Object],[object Object],[object Object],[object Object],Unmixing of Hyperspectral Images ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

Airborne Hyperspectral Images ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

Geometric Correction, Rectification & Calibration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Raw Corrected Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

Grain Sorghum Yield Data Collection Ag Leader PF3000 Yield Monitor Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

Yield Data Crop yield images of the two fields. 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

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

[object Object],[object Object],Fusion of Multi-date Unmixing Results where Y is the yield data 26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

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

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

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

Conclusions ,[object Object],[object Object],26-July-2011 Linear Unmixing of Multidate Hyperspectral Imagery for Crop Yield Estimation

Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.

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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

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

11.

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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Editor's Notes

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

Luo-IGARSS2011-2385.ppt

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Luo-IGARSS2011-2385.ppt

Editor's Notes