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|Type:||Artigo de Periódico|
|Title:||Hyperspectral Data Classification Improved By Minimum Spanning Forests|
|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)|
Minimum Spanning Forests
|Editor:||SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS|
|Citation:||Journal Of Applied Remote Sensing. SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, n. 10, n. 25007, p. .|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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