Image and Signal Processing Methods

Hyperspectral data classification improved by minimum spanning forests

[+] Author Affiliations
Ricardo Dutra da Silva

Federal University of Technology, Informatics Department, Av. Sete de Setembro 3165, Curitiba, Paraná 80230-901, Brazil

Helio Pedrini

University of Campinas, Institute of Computing, Av. Albert Einstein 1251, Campinas, São Paulo 13083-852, Brazil

J. Appl. Remote Sens. 10(2), 025007 (Apr 26, 2016). doi:10.1117/1.JRS.10.025007
History: Received November 8, 2015; Accepted March 24, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Ricardo Dutra da Silva and Helio Pedrini
"Hyperspectral data classification improved by minimum spanning forests", J. Appl. Remote Sens. 10(2), 025007 (Apr 26, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.025007


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