A Deep Vector Quantization Clustering Method for Polarimetric SAR Images

Abstract

Convolutional Neural Network (CNN) models are widely used in supervised Polarimetric Synthetic Aperture Radar (PolSAR) image classification. They are powerful tools to capture the non-linear dependency between adjacent pixels and outperform traditional methods on various benchmarks. On the contrary, research works investigating unsupervised PolSAR classification are quite rare, because most CNN models need to be trained with labeled data. In this paper, we propose a completely unsupervised model by fusing the Convolutional Autoencoder (CAE) with Vector Quantization (VQ). An auxiliary Gaussian smoothing loss is adopted for better semantic consistency in the output classification map. Qualitative and quantitative experiments are carried out on satellite and airborne full polarization data (RadarSat2/E-SAR, AIRSAR). The proposed model achieves 91.87%, 83.58% and 96.93% overall accuracy (OA) on the three datasets, which are much higher than the traditional H/alpha-Wishart method, and it exhibits better visual quality as well.

Keywords

• CNN model
• PolSAR image
• unsupervised classification
• convolutional autoencoder
• vector quantization
• Gaussian smoothing loss