Example based super resolution land cover mapping using support vector regression
Por: Yihang, Zhang ... [et al.]
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Colaborador(es): Yun, Du | Feng, Ling | Shiming, Fang | Xiaodong, Li.
Tipo de material: 
ArtículoTema(s): Satélites artificiales en detección a distancia | Satélites geodésicos | Prospección geofísica | Reconocimientos topográficos | Procesamiento óptico de datos | Reconocimiento de modelos bosques, suelos, edificios, plantaciones, etc | Sistemas de imágenes | Procesamiento de imágenes | Análisis espectral | Espectrómetros 1 | Fotogrametría 1 | Cartografía 1 | Percepcion de formas 1 | Percepcion de estructuras
En: Journal of selected topics in applied earth observations and remote sensing Vol. 7, núm. 4 (April, 2014), pp. 1271-1283Resumen: Super resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real world situations, making the resultant fine resolution land cover map often have uncertainty
| 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
Super resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real world situations, making the resultant fine resolution land cover map often have uncertainty