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Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Feb2015, Vol. 53 Issue 2, p952-964, 13p
- Publication Year :
- 2015
-
Abstract
- Segmentation of real-world remote sensing images is challenging because of the large size of those data, particularly for very high resolution imagery. However, a lot of high-level remote sensing methods rely on segmentation at some point and are therefore difficult to assess at full image scale, for real remote sensing applications. In this paper, we define a new property called stability of segmentation algorithms and demonstrate that piece- or tile-wise computation of a stable segmentation algorithm can be achieved with identical results with respect to processing the whole image at once. We also derive a technique to empirically estimate the stability of a given segmentation algorithm and apply it to four different algorithms. Among those algorithms, the mean-shift algorithm is found to be quite unstable. We propose a modified version of this algorithm enforcing its stability and thus allowing for tile-wise computation with identical results. Finally, we present results of this method and discuss the various trends and applications. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 53
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 101187206
- Full Text :
- https://doi.org/10.1109/TGRS.2014.2330857