Classification of polarimetric SAR data based on multidimensional watershed clustering

Authors:
Wen Yang;Hao Wang;Yongfeng Cao;Haijian Zhang
Affiliations:
School of Electronic Information, Wuhan University, Wuhan, Hubei Province, China;School of Electronic Information, Wuhan University, Wuhan, Hubei Province, China;School of Electronic Information, Wuhan University, Wuhan, Hubei Province, China;School of Electronic Information, Wuhan University, Wuhan, Hubei Province, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 2
Cited 0

Geodesic Saliency of Watershed Contours and Hierarchical Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Cluster Formation Using Level Set Methods

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a polarimetric synthetic aperture radar (PolSAR) data classification method which applies multi-dimensional transform to identify density peaks and valleys for polarimetric signatures clustering. The new approach firstly introduces an improved maximum homogeneous region filter which can effectively preserve structure feature and polarimetric signatures. Then polarimetric signatures are extracted based on Freeman-Durden three-component composition. Finally, we obtain the classification results by multi-dimensional watershed clustering on the extracted polarimetric signatures. The effectiveness of this classification scheme is demonstrated using the full polarimetric L-band SAR imagery.