Digital analysis of remotely sensed images for evaluating color in turfgrass Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/kh04dt20p

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Most conventional approaches to rating turf color for different turf varieties grown under similar conditions or for the same variety grown under different cultural conditions employ a visual subjective rating. By digitizing remotely sensed images acquired by use of a helium filled blimp and a Canon EOS camera, we were able to quickly and inexpensively rate turf plots. Our classification techniques evaluate histograms of pixels within a defined area rather than classifying each individual pixel as in traditional classifications. Analysis of the digital images includes separation of the image into the distinct 8-bit planes (red, green and blue). A plot map is created such that each experimental unit is defined by a rectangle. When this plot map is superimposed over the registered red, green and blue images, the respective histograms are collected for all plots. Data from these histograms are related to visual ratings for each plot. Supervised classification employs least squares regression equations developed from the histogram data and visual evaluations for the respective reference plots. Relationships apparent in the reference plots are used to predict values for all plots. Unsupervised classification is achieved by applying principal components transformation to the histogram data. The transformed values are then scaled to fit within the traditional 1-10 turf rating system by supplying maximum and minimum values. Supervised classifications produce predicted color values that are similar to visual ratings, but with greater statistical significance and fewer violations of the assumptions for ANOVA. Unsupervised classifications were similar to supervised classifications in two of three trials, but not consistently related to visual ratings in a third.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome, 256 Grayscale, 24-bit Color) using Scamax Scan+ V.1.0.32.10766 on a Scanmax 412CD by InoTec in PDF format. LuraDocument PDF Compressor V.5.8.71.50 used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Submitted by Erin Clark (ecscannerosu@gmail.com) on 2011-11-10T19:22:53Z No. of bitstreams: 1 FRIEDKINWILLIAMC1997.pdf: 1678701 bytes, checksum: e57b0ceb1fa607709fa6562e500664ea (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2011-11-17T22:46:53Z (GMT) No. of bitstreams: 1 FRIEDKINWILLIAMC1997.pdf: 1678701 bytes, checksum: e57b0ceb1fa607709fa6562e500664ea (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2011-11-10T23:14:29Z (GMT) No. of bitstreams: 1 FRIEDKINWILLIAMC1997.pdf: 1678701 bytes, checksum: e57b0ceb1fa607709fa6562e500664ea (MD5)
  • description.provenance : Made available in DSpace on 2011-11-17T22:46:53Z (GMT). No. of bitstreams: 1 FRIEDKINWILLIAMC1997.pdf: 1678701 bytes, checksum: e57b0ceb1fa607709fa6562e500664ea (MD5) Previous issue date: 1996-12-06

Relationships

In Administrative Set:
Last modified: 08/19/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items