Title
An Unsupervised Method For Equivalent Number Of Looks Estimation In Complex Sar Scenes
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.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
equivalent number of looks (ENL), unsupervised estimation, complex SAR scene
Field
DocType
ISSN
Histogram,Computer vision,Data modeling,Pattern recognition,Speckle pattern,Synthetic aperture radar,Segmentation,Computer science,Image segmentation,Unsupervised learning,Artificial intelligence,Estimator
Conference
2153-6996
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Dingsheng Hu111.38
Anthony P. Doulgeris2334.62
Xiaolan Qiu319026.75