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Using image hierarchies to interpret LANDSAT data Catanzariti, Ezio
Abstract
Most automatic LANDSAT image interpretation systems have used traditional Pattern Recognition techniques. Usually each pixel is classified into one of a number of categories by examining its spectral signature, without regard to its spatial context. A survey of such techniques and of computational vision techniques from Artificial Intelligence leads to the design of a new system that allows the spatial structure of the image to control the interpretation. This classifier uses a pyramidal, hierarchical structure of images. A number of experiments with the implementation on LANDSAT images of forest cover show that one can achieve improvements over a conventional classifier in both accuracy (number of pixels correctly interpreted) and readability (number of regions in the interpreted image) without sacrificing efficiency.
Item Metadata
Title |
Using image hierarchies to interpret LANDSAT data
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1977
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Description |
Most automatic LANDSAT image interpretation systems have used traditional Pattern Recognition techniques. Usually each pixel is classified into one of a number of categories by examining its spectral signature, without regard to its spatial context. A survey of such techniques and of computational vision techniques from Artificial Intelligence leads to the design of a new system that allows the spatial structure of the image to control the interpretation. This classifier uses a pyramidal, hierarchical structure of images. A number of experiments with the implementation on LANDSAT images of forest cover show that one can achieve improvements over a conventional classifier in both accuracy (number of pixels correctly interpreted) and readability (number of regions in the interpreted image) without sacrificing efficiency.
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Language |
eng
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Date Available |
2010-02-16
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Provider |
Vancouver : University of British Columbia Library
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Rights |
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.
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DOI |
10.14288/1.0051788
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Affiliation | |
Degree Grantor |
University of British Columbia
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Rights
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.