Mean shift-based clustering of remotely sensed data

Lior Friedman, Nathan S. Netanyahu, Maxim Shoshany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageAmerican English
Title of host publicationGeoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International
StatePublished - 2003

Bibliographical note

Place of conference:Toulouse, France


Dive into the research topics of 'Mean shift-based clustering of remotely sensed data'. Together they form a unique fingerprint.

Cite this