Phenology Alignment-Based PolSAR Crop Classification Considering Polarimetric Statistical and Time-Varying Curve Characteristics

Abstract

The uncertainty of crop phenological cycle is an important issue in crop classification with time series PolSAR data. The time series alignment algorithm represented by dynamic time warping (DTW) can supply a potential solution, which realigns curves based on shape matching, dealing with the distortion of feature curves caused by uncertain crop phenological development. However, previous studies mainly focused on shape characteristics of time-varying feature curves, which is hard to comprehensively evaluate the similarity degree of crop phenological cycles. Furthermore, it ignored the differences in scattering signal and polarimetric statistical distribution of crops, which limited the accuracy of crop classification. In this letter, a novel crop classification method based on phenology alignment is proposed. Firstly, the dual-branch time series alignment method is proposed, including the time-weighted dynamic time warping (TWDTW) alignment and the Wishart distance-based TWDTW (WD-TWDTW) alignment, which combines the feature curve characteristics and the polarimetric statistical information to correctly describe the similarity degree of phenological cycles. Secondly, a multi-similarity measure (including shape similarity, feature similarity and polarimetric similarity) is defined to improve capacity of crop discrimination. The multi-similarity measure can describe the differences of crop types from three aspects, including crop growth trend, growth status, and statistical distribution. The proposed method is evaluated with time series full-polarization Radarsat-2 data in Flevoland area. The results show that our method is superior to traditional method with single TWDTW alignment and shape similarity, and the corresponding overall accuracy is improved by 6%.