A time-series classification approach based on change detection for rapid land cover mapping

Abstract

Land-Use/Land-Cover Time-Series Classification (LULC-TSC) is an important and challenging problem in terrestrial remote sensing. Detecting change-points, dividing the entire time series into multiple invariant subsequences, and classifying the subsequences can improve LULC classification efficiency. Therefore, we have proposed a Time-Series Classification approach based on Change Detection (TSCCD) for rapid LULC mapping that uses the Prophet algorithm to detect the ground-cover change-points and perform time-series segmentation in a time dimension and the DTW (Dynamic Time Warping) algorithm to classify the sub-time series. Since we can assume that the ground cover remains unchanged in each subsequence, only one time-training sample selection and one LULC classification are needed, which greatly improves the work efficiency. Prophet can accurately detect large and subtle changes, capture change direction and change rate, and is strongly robust for handling noise and missing data. DTW is mainly used to improve the accuracy of time-series classification and to resolve the time misalignment problems of ground-cover series data caused by irregular observations or missing values. The results of comparative experiments with BFAST, LandTrendR, and CCDC using simulated time-series showed that TSCCD can detect large and subtle changes and capture change direction and change rate, performing substantially better than the other three contrasting algorithms overall in time-series change detection. Finally, the MODIS (Moderate Resolution Imaging Spectroradiometer) time-series images of Wuhan City from 2000 to 2018 were selected for TSCCD, and the results of China’s national land-use surveys in 2000, 2005, 2008, 2010, 2013, and 2015 were used for cross-validation. The results showed that the classification accuracy of each tested subsequence was higher than 90% and that most Kappa coefficients were greater than 0.9. This means that the proposed TSCCD approach can effectively solve real LULC-TSC problems and has high application value. It can be used for large-area, long time-series LULC classification, which is of great guiding significance for studying global environmental changes, forest-cover changes, and conducting land-use surveys.