NDLI: Segmentation and labeling of polarimetric SAR data: can wavelets help?

Content Provider	IEEE Xplore Digital Library
Author	De Grandi, G.F. Lee, J.S. Siqueira, R. Baraldi, A. Simard, M.
Copyright Year	2001
Description	Author affiliation: Eur. Comm. Joint Res. Centre, Space Applications Inst., Ispra, Italy (De Grandi, G.F.)
Abstract	In this paper we report about a novel approach to segmentation and thematic labeling of SAR polarimetric data. A pre-processing phase based on a wavelet frame that works as a differential operator generates piece-wise smooth approximations of the covariance matrix power term images. This step matches the signal characteristics with the requirements of well proven and computationally robust clustering algorithms of the Bayesian MAP, hard or soft labeling, and contextual type. Segments defined in this first phase are then used to estimate polarimetric quantities such as the Cloude's and Pottier's target decomposition parameters. The advantage over methods that use local statistics in a moving window is that the variance of the estimators decreases with the segment size, good accuracy can be obtained without sacrificing spatial resolution, and errors due to the signal discontinuities are avoided. The passage is then made from pixel based clustering to segment wise thematic classification. To the purpose reference feature vectors based on ground truth are needed, and segments are reassigned to the thematic labels. The per-segment feature vectors, including averages of polarimetric, radiometric quantities, or texture measures, can also be exploited to derive biophysical parameters and qualify additionally the classification categories. The proposed approach seems to be appealing because it can tackle under a unified framework resting on solid mathematical foundations - such as Bayesian inference and wavelet theory - different data sources (microwave and optical) and different thematic contexts.
Starting Page	410
Ending Page	413
File Size	176439
Page Count	4
File Format	PDF
ISBN	0780370317
DOI	10.1109/IGARSS.2001.976174
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Image segmentation Bayesian methods Power generation Covariance matrix Robustness Clustering algorithms Phase estimation Error analysis Spatial resolution
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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