Graduate Thesis Or Dissertation

 

A per-segment approach to improving aspen mapping from remote sensing imagery and its implications at different scales Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/5m60qv38r

Descriptions

Attribute NameValues
Creator
Abstract
  • A per-segment classification system was developed to map aspen (Populus tremuloides) stands on Winter Ridge in central Oregon from remote sensing imagery. A 1-meter color infrared (CIR) image was segmented based on its hue and saturation values to generate aspen "candidates", which were then classified to show aspen coverage according to the mean values of spectral reflectance and multi-resolution texture within the segments. For a three-category mapping, an 88 percent overall accuracy with a K-hat statistic of 0.82 was achieved, while for a two-category mapping, a 90 percent overall accuracy with a K-hat statistic of 0.78 was obtained. In order to compare these results to traditional per-pixel classifications, an unsupervised classification procedure based on the ISODATA algorithm was applied to both pixel-based and segment-based seven-layer images. While differences among various per-pixel classifications were found to be insignificant, the results from the per-segment system were consistently more than 20 percent better than those from per-pixel classifications. Both the per-segment and per-pixel classifications were applied at various spatial resolutions in order to study the effect of spatial resolution on the relative performance of the two methods. The per-segment classifier outperformed the per-pixel classifier at the 1-4-m resolution, performed equally well at the 8-16-m resolution and showed no ability to classify accurately at the 32-m resolution due to the segmentation process used. Overall, the per-segment method was found to be more scale-sensitive than the per-pixel method and required some tuning to the segmentation algorithm at lower resolutions. These results illustrate the advantages of per-segment methods at high spatial resolutions but also suggest that segmentation algorithms should be applied carefully at different spatial resolutions.
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
Publisher
Peer Reviewed
Language
Digitization Specifications
  • Master files scanned at 600 ppi (256 Grayscale, 24-bit Color) using Capture Perfect 3.0 on a Canon DR-9080C in TIF format. PDF derivative scanned at 300 ppi (256 greyscale, 24-bit Color), using Capture Perfect 3.0, on a Canon DR-9080C. CVista PdfCompressor 3.1 was used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Linda Kathman(linda.kathman@oregonstate.edu) on 2008-07-21T22:25:59Z (GMT) No. of bitstreams: 1 Heyman_Ofer_2003.pdf: 4593142 bytes, checksum: 9726b786f32ef6cdc25dc933ad6c94a5 (MD5)
  • description.provenance : Made available in DSpace on 2008-07-21T22:28:23Z (GMT). No. of bitstreams: 1 Heyman_Ofer_2003.pdf: 4593142 bytes, checksum: 9726b786f32ef6cdc25dc933ad6c94a5 (MD5)
  • description.provenance : Submitted by Sara Mintonye (smscanner@gmail.com) on 2008-07-14T20:16:53Z No. of bitstreams: 1 Heyman_Ofer_2003.pdf: 4593142 bytes, checksum: 9726b786f32ef6cdc25dc933ad6c94a5 (MD5)
  • description.provenance : Approved for entry into archive by Linda Kathman(linda.kathman@oregonstate.edu) on 2008-07-21T22:28:22Z (GMT) No. of bitstreams: 1 Heyman_Ofer_2003.pdf: 4593142 bytes, checksum: 9726b786f32ef6cdc25dc933ad6c94a5 (MD5)

Relationships

Parents:

This work has no parents.

Items