An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery
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.
Authors who publish with Engineering Journal agree to transfer all copyright rights in and to the above work to the Engineering Journal (EJ)'s Editorial Board so that EJ's Editorial Board shall have the right to publish the work for nonprofit use in any media or form. In return, authors retain: (1) all proprietary rights other than copyright; (2) re-use of all or part of the above paper in their other work; (3) right to reproduce or authorize others to reproduce the above paper for authors' personal use or for company use if the source and EJ's copyright notice is indicated, and if the reproduction is not made for the purpose of sale.