Vegetable classification in Indonesia using Dynamic Time Warping of Sentinel-1A dual polarization SAR time series

Abstract

This study investigates the potential of Sentinel-1A (S1A) dual polarization SAR time series data for vegetable classification in Indonesia. Vegetables are characterized by the temporal changes of observables extracted from time series of S1A data. We extracted observables regarding both backscatter (VH and VV) coefficients and decomposition features (i.e., entropy, angle, and anisotropy). The vegetable classification is based on a time-weighted Dynamic Time Warping dissimilarity measure that is calculated with SPRING strategy for subsequence searching, referred to as twDTWS. This study focuses on three main vegetable types widely planted in Indonesia, namely chili, tomato, and cucumber. We conducted vegetable classification in two areas, Malang and Lampung, using time series of S1A data covering the dry season in 2017. Our results show that the twDTWS method provides a promising means to classify vegetables using time series of S1A data for the dry season, while the features decomposed from dual polarization S1A data have little influence on the classification accuracy. Moreover, the twDTWS method with query sequences (namely reference temporal profiles) defined on the Malang dataset produced an overall accuracy of 0.80 for the classification of chili and cucumber from the Lampung dataset when the query sequences correspond to the growth cycles of vegetables. The variation in the length (i.e., the number of observations) of query sequences can affect the classification accuracy. We conclude that the twDTWS method has a high potential for classifying vegetables in different areas when constructing the query sequences of vegetables based on their growth cycles.