NDLI: An unsupervised method for equivalent number of looks estimation in complex SAR scenes

Content Provider	IEEE Xplore Digital Library
Author	Dingsheng Hu Doulgeris, A.P. Xiaolan Qiu
Copyright Year	2015
Description	Author affiliation: Dept. of Phys. & Technol., Univ. of Tromso, Tromso, Norway (Dingsheng Hu; Doulgeris, A.P.) \|\| Inst. of Electron., Beijing, China (Xiaolan Qiu)
Abstract	This paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties of ENL data into an automatic segmentation method, we isolate the sub-class affected least by mixture and texture and suggest taking the mean value of this class as the final ENL estimate. The proposed estimator is evaluated in the experiments performed on simulated and real data from two very different sensors. It always gives better results than the other two existing methods and possesses greater adaptability.
Starting Page	2465
Ending Page	2468
File Size	624724
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326309
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Synthetic aperture radar Histograms Data models Image segmentation Robustness complex SAR scene equivalent number of looks (ENL) unsupervised estimation
Content Type	Text
Resource Type	Article

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