A Novel Multi-Scale CNN Model for Crop Classification with Time-Series Fully Polarization SAR Images

Abstract

Crop classification is one of the most significant applications for Polarimetric Synthetic Aperture Radar (PolSAR) data. Owing to the limited information obtained by the single-temporal PolSAR data, the multi-temporal data are used in this paper to further provide ample information within various crop growing stages. However, the polarization scattering decomposition for multi-temporal PolSAR data easily causes “dimension disaster”. Hence, a neural network of sparse auto-encoder with non-negativity constraints (NC-SAE) was employed to compress the data, yielding efficient features for accurate classification. Then, a novel classifier of multi-scale feature classification network (MSFCN) was constructed to improve the classification performance, which is proved to be superior to the popular classifiers of convolutional neural networks (CNN) and supper vector machine (SVM). The performances of the proposed method were evaluated and compared with the traditional methods by using simulated Sentinel-1 data provided by European Space Agency (ESA). The classification results indicate that the proposed methodology is promising for practical use in agricultural applications.