Comparative assessment of supervised classifiers for land use land cover classification in a tropical region using time series PALSAR mosaic data
Por: Tomohiro, Shiraishi ... [et al.]
.
Colaborador(es): Takeshi, Motohka | Bahadur Thapa, Rajesh | Manabu, Watanabe | Masanobu, Shimada.
Tipo de material: 
ArtículoTema(s): Satélites artificiales en detección a distancia | Satélites geodésicos | Prospección geofísica | Reconocimientos topográficos | Uso de la tierra | Procesamiento óptico de datos | Reconocimiento de bosques, suelos, edificios, plantaciones, etc | Sistemas de imágenes | Procesamiento de imágenes
En: Vol. 7, núm. 4 April, 2014, pp. 1186-1199 Journal of selected topics in applied earth observations and remote sensing R0141542Resumen: Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms are possible. This research investigated the accuracy and processing speeds of five supervised classifiers, including Naive Bayes, AdaBoost, multi layer perceptron, random forest (RF) and support vector machine, for land use-land cover (LULC) clasification in a tropical region using time series advanced land observing satellite phased array type L-band SAR (ALOS-PALSAR) 25-m mosaic data
| Tipo de ítem | Biblioteca de origen | Colección | Signatura | Vol info | Copia número | Estado | Fecha de vencimiento | Código de barras | Reserva de ejemplares |
|---|---|---|---|---|---|---|---|---|---|
Revista
|
Campus II | Hemeroteca | PP-2 550 I592j (Navegar estantería) | Vol. 7, núm. 4 (April, 2014) | Ej. 1 | Disponible | R0141542 |
CAMPUS II
Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms are possible. This research investigated the accuracy and processing speeds of five supervised classifiers, including Naive Bayes, AdaBoost, multi layer perceptron, random forest (RF) and support vector machine, for land use-land cover (LULC) clasification in a tropical region using time series advanced land observing satellite phased array type L-band SAR (ALOS-PALSAR) 25-m mosaic data