An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

  • Lawankorn Soimart King Mongkut's University of Technology North Bangkok
  • Mahasak Ketcham King Mongkut's University of Technology North Bangkok

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Abstract

Many image segmentation algorithms are available but most of them are not fit for interpretation of satellite images. Mean-shift algorithm has been used in many recent researches as a promising image segmentation technique, which has the speed at O(kn2) where n is the number of data points and k is the number of average iteration steps for each data point. This method computes using a brute-force in the iteration of a pixel to compare with the region it is in. This paper proposes a novel algorithm named First-order Neighborhood Mean-shift (FNM) segmentation, which is enhanced from Mean-shift segmentation. This algorithm provides information about the relationship of a pixel with its neighbors; and makes them fall into the same region which improve the speed to O(kn). In this experiment, FNM were compared to well-known algorithms, i.e., K-mean (KM), Constrained K-mean (CKM), Adaptive K-mean (AKM), Fuzzy C-mean (FCM) and Mean-shift (MS) using the reference map from Landsat. FNM provided better results in terms of overall error and correctness criteria.

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Author Biographies
Lawankorn Soimart

Faculty of Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

Mahasak Ketcham

Faculty of Information Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

Published
Vol 20 No 5, Nov 25, 2016
How to Cite
L. Soimart and M. Ketcham, “An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery”, Eng. J., vol. 20, no. 5, pp. 215-228, Nov. 2016.

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