Automatic interpretation of Landsat images using context sensitive region merging

Automatic interpretation of images from Earth Resources Technology Satellite-1 (ERTS-1, now called LANDSAT) can be used in a variety of applications with considerable accuracy. Most systems, however, classify strictly on a point by point basis, making no use of any spatial knowledge. Standard photo-...

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Main Author: Starr, Dale William
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/2429/19834
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spelling ndltd-UBC-oai-circle.library.ubc.ca-2429-198342018-01-05T17:40:14Z Automatic interpretation of Landsat images using context sensitive region merging Starr, Dale William Earth Resources Technology Satellite Remote sensing Scientific satellites Automatic interpretation of images from Earth Resources Technology Satellite-1 (ERTS-1, now called LANDSAT) can be used in a variety of applications with considerable accuracy. Most systems, however, classify strictly on a point by point basis, making no use of any spatial knowledge. Standard photo-interpretation techniques are combined with some techniques from artificial intelligence to produce an increase in accuracy over a point-by-point classification method. Traditional classification methods are used to obtain an initial segmentation of the image. Then, a controlled region merging process allows the regions with unambiguous interpretations to influence the interpretation of neighbouring ambiguous regions, thereby introducing considerable context sensitivity into the interpretation process. Results are given of an experiment to interpret areas of different forest cover. Science, Faculty of Computer Science, Department of Graduate 2010-02-08T22:33:03Z 2010-02-08T22:33:03Z 1976 Text Thesis/Dissertation http://hdl.handle.net/2429/19834 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
collection NDLTD
language English
sources NDLTD
topic Earth Resources Technology Satellite
Remote sensing
Scientific satellites
spellingShingle Earth Resources Technology Satellite
Remote sensing
Scientific satellites
Starr, Dale William
Automatic interpretation of Landsat images using context sensitive region merging
description Automatic interpretation of images from Earth Resources Technology Satellite-1 (ERTS-1, now called LANDSAT) can be used in a variety of applications with considerable accuracy. Most systems, however, classify strictly on a point by point basis, making no use of any spatial knowledge. Standard photo-interpretation techniques are combined with some techniques from artificial intelligence to produce an increase in accuracy over a point-by-point classification method. Traditional classification methods are used to obtain an initial segmentation of the image. Then, a controlled region merging process allows the regions with unambiguous interpretations to influence the interpretation of neighbouring ambiguous regions, thereby introducing considerable context sensitivity into the interpretation process. Results are given of an experiment to interpret areas of different forest cover. === Science, Faculty of === Computer Science, Department of === Graduate
author Starr, Dale William
author_facet Starr, Dale William
author_sort Starr, Dale William
title Automatic interpretation of Landsat images using context sensitive region merging
title_short Automatic interpretation of Landsat images using context sensitive region merging
title_full Automatic interpretation of Landsat images using context sensitive region merging
title_fullStr Automatic interpretation of Landsat images using context sensitive region merging
title_full_unstemmed Automatic interpretation of Landsat images using context sensitive region merging
title_sort automatic interpretation of landsat images using context sensitive region merging
publishDate 2010
url http://hdl.handle.net/2429/19834
work_keys_str_mv AT starrdalewilliam automaticinterpretationoflandsatimagesusingcontextsensitiveregionmerging
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