An Efficient Approach to Detect Sudden Changes in Vegetation Index Time Series for Land Change Detection

摘要

In this paper, we proposed a novel data mining approach Recursive Search Algorithm (RSA) to detect sudden changes in time series data-set. In literature, Modified Lunetta, cumulative sum (CUMSUM) MEAN, Yearly Delta, and Recursive Merging techniques are used to detect sudden changes in land covers using data mining approach. The main drawback of Modified Lunetta, CUMSUM MEAN, and Yearly Delta approach is that, it only identifies a time series is changed or not, while Recursive Merging technique finds the changed segment only. RSA approach has the capability to detect with high confidence to correctly compute the change point (time of change) in time series data, also detect the type of change (increase/decrease) occurred in series. The proposed algorithm is scalable, considerable improvement in performance in the presence of cyclic data. All experiments are performed on synthetic data-set, which is analogous to vegetation index time series data-set.