Optimal Threshold Value Determination for
Land Change Detection
Sangram Panigrahi1,
Kesari Verma1, and Priyanka Tripathi2
1Department of Computer
Applications, National Institute of
Technology Raipur,
India
2Department of Computer Engineering and Applications, National Institute of Technical
Teachers Trainingand Research Bhopal, India
Abstract: Recently data mining techniques
have emerged as an important technique to detect land change by detecting the
sudden change and/or gradual change in time series of vegetation index dataset.
In this technique, the algorithms takes the vegetation index time series data
set as input and provides a list of change scores as output and each change
score corresponding to a particular location. If the change score of a location
is greater than some threshold value, then that location is considered as change.
In this paper, we proposed a two step process for threshold determination:
first step determine the upper and lower boundary for threshold and second step
find the optimal point between upper and lower boundary, for change detection
algorithm. Further, by engaging this process, we determine the threshold value
for both Recursive Merging Algorithm and Recursive Search Algorithm and
presented a comparative study of these algorithms for detecting changes in time
series data. These techniques are evaluated quantitatively using synthetic
dataset, which is analogous to vegetation index time series data set. The
quantitative evaluation of the algorithms shows that the Recursive Merging (RM)
method performs reasonably well, but the Recursive Search Algorithm (RSA)
significantly outperforms in the presence of cyclic data.
Keywords: Data mining, threshold determination,
EVI and NDVI time series data, high dimensional data, land change detection, recursive
search algorithm, recursive merging algorithm.