A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data

Abstract

The use of SAR data for land cover mapping provides many advantages over land cover classification achieved using optical remote sensing data. However, the classification of SAR data has always been a challenging task. In this study, the feasibility of the use of semantic segmentation based deep learning networks to classify temporal SAR data has been demonstrated. It has been achieved by applying six deep learning architectures viz. Pyramid Scene Parsing, UNET, DeepLabv3+, Path Aggregation Network, Encoder-Decoder Network and Feature Pyramid Network over temporally acquired SAR datasets for three different frequencies (triannual, quarterly and bimonthly). Outputs of all six architectures have been assessed using frequency weighted IoU. It was observed that Pyramid Scene Parsing architecture when applied on bimonthly temporal SAR provides the best results.