Multi-temporal SAR image classification of coastal plain wetlands using a new feature selection method and random forests

Abstract

Efficient and reliable wetland mapping is essential for wetland resource management and conservation planning. Repeated attempts at classifying dense wetland vegetation has often failed to produce a high-quality product because of poor penetration of electromagnetic signals that cannot detect sub-surface water, and because of associated redundant information. In this study, a two-step classification procedure was developed for classifying typical coastal plain wetlands from multi-temporal Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR)-1 data. First, a new statistical strategy for feature selection was proposed by combining manual and automatic selection according to the backscattering mechanism differences between various land-cover types. Then the Random Forest (RF) classifier was applied to evaluate the established feature combinations for wetland classification. The experimental results show that the method described has high classification accuracies over the test site, with overall accuracies and kappa coefficients (κ) of above 89.79% and 0.88, respectively. This indicates that the presented technique is effective in dealing with high-dimensional feature space and may lead to significant improvements in multi-temporal Synthetic Aperture Radar (SAR) image-based wetland classification.