Satellite used

Study region



TerraSAR-X [20]

Pleine Fougères, Bay of Mont-Saint-Michel, Franc

Different multitemporal classification techniques

Use of multitemporal TerraSAR-X images for herbaceous wetland mapping and vegetation formations is found to be very accurate




Random Forest classifier

Combination of LANDSAT and PALSAR data gives the best classification results for land cover classification

Radarsat-2, AgriSAR and RapidEye [19]

Indian Head (Canada) agricultural site area

Maximum Likelihood (MXL) as supervised method

multi-source data can deliver within land-cover, and more specifically crop type, mapping application

AIRSAR Airborne data [28]

French Polynesia islands located at the middle of the South Paciļ¬c Ocean

SVM (Support Vector Machine)

SVM for polarimetric SAR classification was found to be relevant for land use cartography

PALSAR Image [29]

Roorkee, Laksar, Bijnor regions, Uttarkhand, India

Decision Tree classifier and supervised classifiers

The decision tree classifier recognizes all land cover types more accurately from training pixels than parallelepiped, minimum distance, MXL and Wishart classifiers

TerraSAR-X [30]

Foshan in central Guangdong province, China

Supervised hierarchical Markov aspect models (HMAM)

TerraSAR-X SAR terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training.

Radarsat-1 SAR image [31]

Jeollabuk-do area in Korea

Object-based classification

SAR amplitude imagery and terrain information by object-based classification is accurate and significantly improved