Data/hora: 14/06/2024 07:44:04

Biblioteca(s): 	Embrapa Solos.	
Data corrente: 	12/12/2022	
Data da última atualização: 	12/12/2022	
Autoria: 	BASTOS, B. P.; PINHEIRO, H. S. K; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos; FERREIRA, F. J. F.	
Afiliação: 	BLENDA PEREIRA BASTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; WALDIR DE CARVALHO JUNIOR, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; FRANCISCO JOSÉ FONSECA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ.	
Título: 	Clustering airborne gamma-ray spectrometry data in Nova Friburgo, State of Rio de Janeiro, southeastern Brazil.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Journal of Applied Geophysics, v. 209, 104900, Feb. 2023.	
DOI: 	https://doi.org/10.1016/j.jappgeo.2022.104900	
Idioma: 	Inglês	
Conteúdo: 	The goal of this study was to test different image clustering techniques, using airborne gamma-ray spectrometry data to optimize the interpretation of areas with similar properties in large scale. The methodology applied was based on the comparison of two techniques of data-driven classification (K-means and Gaussian Mixture Models) and a technique of knowledge-driven classification (Simplified RGB) to discriminate domains from the primary variables potassium (K), uranium (eU), and thorium (eTh) obtained from airborne gamma-ray spectrometry surveys. The performance of these three methods was evaluated through pre-processing techniques and post-processing including the best number of clusters/classes, visual interpretation, internal validation, and boxplot analysis. The clustering performance was considered satisfactory through the visual interpretation and comparison with the geological map and the DEM, for all three methods. For a quantitative analysis, the simplest model of unsupervised clustering Gaussian Mixture Models prevailed.	
Palavras-Chave: 	Data-driven classification; Gamma-ray spectrometry; Geological mapping; Knowledge-driven classification.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Solos (CNPS)	
Exemplares: 	CNPS - (21207-1) - UPC - AP - DD -  -  -  - DIS