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Journal of Applied Remote Sensing

Hyperspectral data classification improved by minimum spanning forests
Author(s): Ricardo Dutra da Silva; Helio Pedrini
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Paper 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.

Paper Details

Date Published: 26 April 2016
PDF: 17 pages
J. Appl. Rem. Sens. 10(2) 025007 doi: 10.1117/1.JRS.10.025007
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Ricardo Dutra da Silva, Univ. Tecnológica Federal do Paraná (Brazil)
Helio Pedrini, Univ. Estadual de Campinas (Brazil)

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