Title :
Mean shift-based clustering of remotely sensed data
Author :
Friedman, Lior ; Netanyahu, Nathan S. ; Shoshany, Maxim
Author_Institution :
Bar-Ilan Univ., Ramat-Gan, Israel
Abstract :
In this paper, we investigate how to further exploit the various characteristics of mean shift, in an attempt to achieve a robust and efficient clustering module for remotely sensed data. A mean shift algorithm has shown o be promising in various image-processing applications, specifically in cluster analysis.
Keywords :
geophysical signal processing; geophysical techniques; image processing; pattern clustering; remote sensing; statistical analysis; cluster analysis; clustering module; image-processing applications; mean shift-based clustering; remotely sensed data; Clustering algorithms; Convergence; Gaussian distribution; Image converters; Image processing; Image segmentation; Iterative algorithms; Kernel; Remote sensing; Training data;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International
Print_ISBN :
0-7803-7929-2
DOI :
10.1109/IGARSS.2003.1294812
