%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3EQCCU5
%@archivingpolicy denypublisher denyfinaldraft
%@resumeid
%@resumeid
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHMA
%X 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.
%8 July-Sept.
%N 3
%T A Comparison of Multisensor Integration Methods for Land Cover Classification in the Brazilian Amazon
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress dutra@dpi.inpe.br
%@secondarytype PRE PI
%K Radar de Abertura Sintética, radar, Digital Image Processing.
%@usergroup administrator
%@usergroup lattes
%@usergroup secretaria.cpa@dir.inpe.br
%@group
%@group
%@group
%@group DPI-OBT-INPE-MCT-BR
%@e-mailaddress dutra@dpi.inpe.br
%F lattes: 9840759640842299 4 LuLiMorDutBat:2011:CoMuIn
%@issn 1548-1603
%2 dpi.inpe.br/plutao/2011/11.23.19.48.13
%@affiliation Indiana Univ, Anthropol Ctr Training & Res Global Environm Chan, Bloomington, IN 47405 USA
%@affiliation Indiana Univ, Anthropol Ctr Training & Res Global Environm Chan, Bloomington, IN 47405 USA
%@affiliation Indiana Univ, Anthropol Ctr Training & Res Global Environm Chan, Bloomington, IN 47405 USA
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Brazilian Agr Res Corp, EMBRAPA Satellite Monitoring, BR-13070115 Sao Paulo, Brazil
%@project National Science Foundation   BCS 0850615
%B GIScience and Remote Sensing
%@versiontype publisher
%P 345-370
%4 dpi.inpe.br/plutao/2011/11.23.19.48
%@documentstage not transferred
%D 2011
%V 48
%@doi 10.2747/1548-1603.48.3.345
%O Setores de Atividade: Agricultura, Pecuária, Produção Florestal, Pesca e Aqüicultura.
%A Lu, Dengsheng,
%A Li, Guiying,
%A Moran, Emilio,
%A Dutra, Luciano Vieira,
%A Batistella, Mateus,
%@dissemination WEBSCI
%@area SRE