Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/320473
Type: Artigo de Periódico
Title: Hyperspectral Data Classification Improved By Minimum Spanning Forests
Author: da Silva
RD; Pedrini
H
Abstract: Remote sensing technology has applications in various knowledge domains, such as agriculture, meteorology, land use, environmental monitoring, military surveillance, and mineral exploration. The increasing advances in image acquisition techniques have allowed the generation of large volumes of data at high spectral resolution with several spectral bands representing images collected simultaneously. We propose and evaluate a supervised classification method composed of three stages. Initially, hyperspectral values and entropy information are employed by support vector machines to produce an initial classification. Then, the K-nearest neighbor technique searches for pixels with high probability of being correctly classified. Finally, minimum spanning forests are applied to these pixels to reclassify the image taking spatial restrictions into consideration. Experiments on several hyperspectral images are conducted to show the effectiveness of the proposed method. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Subject: Hyperspectral Images
Supervised Classification
Minimum Spanning Forests
Image Analysis
Editor: SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
Citation: Journal Of Applied Remote Sensing. SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, n. 10, n. 25007, p. .
Rights: fechado
Identifier DOI: 10.1117/1.JRS.10.025007
Address: http://spie.org/Publications/Journal/10.1117/1.JRS.10.025007
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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