A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon.
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.
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2011-10-03
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Subjects: | Landsat Thematic Mapper, Wavelet multisensor, |
Online Access: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/902113 |
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dig-alice-doc-9021132017-08-16T01:57:19Z A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. Landsat Thematic Mapper Wavelet multisensor Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution. 2014-09-17T07:35:27Z 2014-09-17T07:35:27Z 2011-10-03 2011 2019-05-03T11:11:11Z Artigo de periódico GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011. http://www.alice.cnptia.embrapa.br/alice/handle/doc/902113 10.2747/1548-1603.48.3.345 pt_BR por openAccess |
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Landsat Thematic Mapper Wavelet multisensor Landsat Thematic Mapper Wavelet multisensor |
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Landsat Thematic Mapper Wavelet multisensor Landsat Thematic Mapper Wavelet multisensor LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
description |
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution. |
author2 |
DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
author_facet |
DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. |
format |
Artigo de periódico |
topic_facet |
Landsat Thematic Mapper Wavelet multisensor |
author |
LU, D. LI, G. MORAN, E. DUTRA, L. BATISTELLA, M. |
author_sort |
LU, D. |
title |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
title_short |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
title_full |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
title_fullStr |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
title_full_unstemmed |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
title_sort |
comparison of multisensor integration methods for land cover classification in the brazilian amazon. |
publishDate |
2011-10-03 |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/902113 |
work_keys_str_mv |
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1756019850920067072 |