A generic land cover classification framework for polarimetric SAR images using the optimum Touzi decomposition parameter subset -- an insight on mutual information based feature selection techniques
Por: Banerjee, Biplab
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Colaborador(es): Bhattacharya, Avik | Buddhiraja, Krishna M.
Tipo de material: 
ArtículoTema(s): Satélites artificiales en detección a distancia | Satélites artificiales en meteorología | Procesamiento óptico de datos | Interferometria | Sistemas de imágenes | Procesamiento de imágenes
En: Vol. 7, núm. 4 April, 2014, pp. 1167-1176 Journal of selected topics in applied earth observations and remote sensing R0141542Resumen: This correspondence proposes a generic framework for land cover classification using support vector machine (SVM) classifier for polarimetric synthetic apertures radar (SAR) images considering the optimum Touzi decomposition parametric. Some new concerns have been raised recently with the Cloude-Pottier decomposition. Cloude's scattering type ambiguities may take place for certain scatterers, and some of the Cloude-Pottier's parameters may not be roll-invariant for asymmetric targets
| Tipo de ítem | Biblioteca de origen | Colección | Signatura | Vol info | Copia número | Estado | Fecha de vencimiento | Código de barras | Reserva de ejemplares |
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Revista
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Campus II | Hemeroteca | PP-2 550 I592j (Navegar estantería) | Vol. 7, núm. 4 (April, 2014) | Ej. 1 | Disponible | R0141542 |
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CAMPUS II
This correspondence proposes a generic framework for land cover classification using support vector machine (SVM) classifier for polarimetric synthetic apertures radar (SAR) images considering the optimum Touzi decomposition parametric. Some new concerns have been raised recently with the Cloude-Pottier decomposition. Cloude's scattering type ambiguities may take place for certain scatterers, and some of the Cloude-Pottier's parameters may not be roll-invariant for asymmetric targets