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

ABSTRACT

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.

KEYWORDS:

• Data mining
• EVI and NDVI time series data
• high dimensional data
• land change detection
• recursive search algorithm

Additional information

Notes on contributors

Sangram Panigrahi

Sangram Panigrahi is currently pursuing PhD programme in Department of Computer Applications, National Institute of Technology Raipur, India. He holds both BTech and MTech degree in information technology. His areas of research interests include data mining, neural networks, and WSN. He has authored number of research papers in the above-mentioned areas in national and international journals and conferences.

E-mail: [email protected]

Kesari Verma

Kesari Verma has received a PhD degree in computer science from Pt. RSU Raipur, India, in 2007. She is currently working as an assistant professor in the Department of Computer Applications, National Institute of Technology Raipur, India. She has around 15 years of teaching and research experience. Her research interests include digital image processing and analysis, data mining, pattern classification, biometrics, machine learning, etc. She is guiding several postgraduate and PhD students. She has published more than 40 papers in various reputed international journals and conferences.

E-mail: [email protected]

Priyanka Tripathi

Priyanka Tripathi has received a PhD degree in web engineering from Maulana Azad National Institute of Technology, Bhopal, India, in 2009. She is currently working as an associate professor in the Department of Computer Engineering and Applications, NITTTR, Bhopal, India. She has around 15 years of teaching and research experience as well as two years of industrial experience. Her research interest includes web engineering, ERP, neural network and fuzzy logic, data mining, and software engineering. She is guiding several postgraduate and PhD students. She has published numerous papers in various reputed international journals and conferences.

E-mail: [email protected]