Title :
Analysis of fully polarimetric SAR data based on the Cloude-Pottier decomposition and the complex Wishart classifier
Author :
Fang, Cao ; Wen, Hong ; Yirong, Wu ; Pottier, Eric
Abstract :
An estimation of the number of clusters is proposed for fully polarimetric SAR data analysis, and a corresponding unsupervised segmentation algorithm is also given based on the Cloude-Pottier decomposition and the complex Wishart clustering. The Monte-Carlo Cross-Validation (MCCV) is used to estimate the optimal number of clusters to reveal the inner structure of the data. Since it is a quantitative estimation of the classification performance, the MCCV algorithm also has the potential capability to perform the unsupervised segmentation validation. The effectiveness of the MCCV estimation and the segmentation algorithm is demonstrated using ESAR data acquired.
Keywords :
Monte Carlo methods; geophysical signal processing; image classification; image segmentation; pattern clustering; radar polarimetry; radar signal processing; synthetic aperture radar; Cloude-Pottier decomposition; Monte Carlo cross-validation method; classification performance quantitative estimation; cluster number estimation; complex Wishart classifier; inner data structure; polarimetric SAR data analysis; unsupervised segmentation algorithm; Algorithm design and analysis; Clustering algorithms; Data analysis; Image analysis; Microwave imaging; Microwave technology; Paper technology; Probability density function;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
DOI :
10.1109/IGARSS.2007.4422756
