The roles of textural images in improving land-cover classification in the Brazilian Amazon.
Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m.
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Format: | Artigo de periódico biblioteca |
Language: | English eng |
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2014-12-05
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Subjects: | Land-cover classification, Phased Array type L-band Synthetic Aperture Radar., Landsat Thematic Mapper, Advanced Land Observing Satellite, |
Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1001846 |
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dig-alice-doc-10018462017-08-16T01:35:35Z The roles of textural images in improving land-cover classification in the Brazilian Amazon. LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. DENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m. 2014-12-05T11:11:11Z 2014-12-05T11:11:11Z 2014-12-05 2014 2014-12-09T11:11:11Z Artigo de periódico International Journal of Remote Sensing, v. 35, n. 24, p. 8188-8207, 2014. 0143-1161 http://www.alice.cnptia.embrapa.br/alice/handle/doc/1001846 10.1080/01431161.2014.980920 en eng openAccess |
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Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite |
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Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
description |
Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m. |
author2 |
DENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
author_facet |
DENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. |
format |
Artigo de periódico |
topic_facet |
Land-cover classification Phased Array type L-band Synthetic Aperture Radar. Landsat Thematic Mapper Advanced Land Observing Satellite |
author |
LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. |
author_sort |
LU, D. |
title |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
title_short |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
title_full |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
title_fullStr |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
title_full_unstemmed |
The roles of textural images in improving land-cover classification in the Brazilian Amazon. |
title_sort |
roles of textural images in improving land-cover classification in the brazilian amazon. |
publishDate |
2014-12-05 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1001846 |
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