TY - GEN
T1 - Mean shift-based clustering of remotely sensed data
AU - Friedman, Lior
AU - Netanyahu, Nathan S.
AU - Shoshany, Maxim
N1 - Place of conference:Toulouse, France
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
UR - https://scholar.google.co.il/scholar?q=Mean+Shift-Based+Clustering+for+Remotely+Sensed+Data&btnG=&hl=en&as_sdt=0%2C5
UR - https://scholar.google.co.il/scholar?q=Mean+Shift-Based+of+Remotely+Sensed+Data&btnG=&hl=en&as_sdt=0%2C5
M3 - Conference contribution
BT - Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International
PB - IEEE
ER -