Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/902113
Research center of Embrapa/Collection: Embrapa Territorial - Artigo em periódico indexado (ALICE)
Date Issued: 2011
Type of Material: Artigo em periódico indexado (ALICE)
Authors: LU, D.
LI, G.
MORAN, E.
DUTRA, L.
BATISTELLA, M.
Additional Information: DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM.
Title: A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon.
Publisher: GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011.
Language: pt_BR
Keywords: Landsat Thematic Mapper
Wavelet multisensor
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.
Data Created: 2011-10-03
Appears in Collections:Artigo em periódico indexado (CNPM)

Files in This Item:
File Description SizeFormat 
ComparisonfusionmethodsGisRS2011.pdf7,17 MBAdobe PDFThumbnail
View/Open

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace