Time-Series Polsar Crop Classification Based on Joint Feature Extraction

Zhiyuan Lin, Qiang Yin, Yongsheng Zhou, Jun Ni, Fei Ma

Abstract: Crop classification is one of the most important applications of polarimetric SAR images. Time-series polarimetric SAR images have the characteristics of reflecting the changes of various scattering characteristics of crops in different growth periods. However, since time-series polarimetric SAR needs to combine multiple single polarimetric SAR images, the redundancy between features is multiplied. In this paper, aiming at the problem of feature redundancy, the method of similarity measurement is used to select features from two dimensions of space and time respectively to reduce feature redundancy. Since the sample size of SAR feature images applied in supervised classification is small, it's not suitable for multiple downsampling in CNN, and a suitable classifier based on Transformer is designed. Preliminary experiments on the full polarimetric data verified the effectiveness of the proposed method.