Terrain Classification of Polarimetric Synthetic Aperture Radar Images Based on Deep Learning and Conditional Random Field Model

Abstract

In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.

Keywords

• Polarimetric Synthetic Aperture Radar (PolSAR)
• Terrain classification
• Conditional Random Field (CRF)
• Convolutional Neural Network (CNN)