This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/86056
Description
Title
Hyperspectral Imagery for Precision Agriculture
Author(s)
Yao, Haibo
Issue Date
2004
Doctoral Committee Chair(s)
Lei Tian
Department of Study
Agricultural Engineering
Discipline
Agricultural Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Agricultural
Language
eng
Abstract
The main contribution of this dissertation to hyperspectral image processing is that a hybrid feature selection and feature extraction approach was proposed and implemented for image feature reduction. The general procedure was to select a subset of the original image bands and transform the image band subset to a new image space. The band subset can be regarded as the significant image bands for a given application. The first implementation of the hybrid feature reduction approach was the GA-SPCA algorithm (Genetic Algorithm-based Selective Principal Component Analysis). In the initial experiments, the coefficients of correlation between an image and ground reference data were greater when using GA-SPCA than using standard PCA. To investigate the GA-SPCA algorithm further, a spatial-spectral feature extraction procedure was developed for soil nutrient classification, using a maximum likelihood classifier (SPCA+ML). In this case, feature extraction was based on class separability, measured using class distance. The second implementation of the hybrid feature reduction approach was the development of an automatic vegetation index generation algorithm---EAVI (Evolutionary Algorithm based Vegetation Index generation). The EAVI algorithm was applied to two precision farming applications---finding the temporal relationship between images and corn yield and estimating corn kernel grain quality. It was found that images from the beginning of full canopy coverage to the corn ear formation period provided the best and most stable results for corn yield estimation. The significant image bands were in the red edge region at 700 nm and in the NIR (near infrared) region at 826 nm.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
