Spectral signature generalization and expansion can improve the accuracy of satellite image classification
Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) sig...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Public Library of Science
2010
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/166056 |
| _version_ | 1855543598952480768 |
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| author | Laborte, Alice G. Maunahan, Aileen A. Hijmans, Robert J. |
| author_browse | Hijmans, Robert J. Laborte, Alice G. Maunahan, Aileen A. |
| author_facet | Laborte, Alice G. Maunahan, Aileen A. Hijmans, Robert J. |
| author_sort | Laborte, Alice G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification. |
| format | Journal Article |
| id | CGSpace166056 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2010 |
| publishDateRange | 2010 |
| publishDateSort | 2010 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1660562025-01-24T14:21:05Z Spectral signature generalization and expansion can improve the accuracy of satellite image classification Laborte, Alice G. Maunahan, Aileen A. Hijmans, Robert J. land classification land use landsat satellite imagery thematic mapper laos Conventional supervised classification of satellite images uses a single multi-band image and coincident ground observations to construct spectral signatures of land cover classes. We compared this approach with three alternatives that derive signatures from multiple images and time periods: (1) signature generalization: spectral signatures are derived from multiple images within one season, but perhaps from different years; (2) signature expansion: spectral signatures are created with data from images acquired during different seasons of the same year; and (3) combinations of expansion and generalization. Using data for northern Laos, we assessed the quality of these different signatures to (a) classify the images used to derive the signature, and (b) for use in temporal signature extension, i.e., applying a signature obtained from data of one or several years to images from other years. When applying signatures to the images they were derived from, signature expansion improved accuracy relative to the conventional method, and variability in accuracy declined markedly. In contrast, signature generalization did not improve classification. When applying signatures to images of other years (temporal extension), the conventional method, using a signature derived from a single image, resulted in very low classification accuracy. Signature expansion also performed poorly but multi-year signature generalization performed much better and this appears to be a promising approach in the temporal extension of spectral signatures for satellite image classification. 2010-05-06 2024-12-19T12:55:47Z 2024-12-19T12:55:47Z Journal Article https://hdl.handle.net/10568/166056 en Open Access Public Library of Science Laborte, Alice G.; Maunahan, Aileen A. and Hijmans, Robert J. 2010. Spectral signature generalization and expansion can improve the accuracy of satellite image classification. PLoS ONE, Volume 5 no. 5 p. e10516 |
| spellingShingle | land classification land use landsat satellite imagery thematic mapper laos Laborte, Alice G. Maunahan, Aileen A. Hijmans, Robert J. Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title | Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title_full | Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title_fullStr | Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title_full_unstemmed | Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title_short | Spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| title_sort | spectral signature generalization and expansion can improve the accuracy of satellite image classification |
| topic | land classification land use landsat satellite imagery thematic mapper laos |
| url | https://hdl.handle.net/10568/166056 |
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