Data/hora: 08/06/2024 02:41:45

Biblioteca(s): 	Embrapa Territorial.	
Data corrente: 	17/05/2012	
Data da última atualização: 	16/09/2014	
Autoria: 	LI, G.; LU, D.; DUTRA, L.; BATISTELLA, M.	
Afiliação: 	GUIYING LI, INDIANA UNIVERSITY; DENGSHENG LU, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM.	
Título: 	A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.	
Ano de publicação: 	2012	
Fonte/Imprenta: 	ISPRS Journal of Photogrammetry and Remote Sensing, v. 70, p. 26-38, 2012.	
Páginas: 	p. 26-38.	
Idioma: 	Inglês	
Conteúdo: 	This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.	
Palavras-Chave: 	ALOS PALSAR; Amazon; Land-cover classification; RADARSAT.	
Thesaurus Nal: 	texture.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/59517/1/MateusISPRS.pdf	
Registro original: 	Embrapa Territorial (CNPM)	
Exemplares: 	CNPM - (3318-1) - UPC - AP - PP - 12/002 - AP - 2012.002 - DIS