Title
Unsupervised Mixture-Eliminating Estimation of Equivalent Number of Looks for PolSAR Data.
Abstract
This paper addresses the impact of mixtures between classes on equivalent number of looks (ENL) estimation. We propose an unsupervised ENL estimator for polarimetric synthetic aperture radar (PolSAR) data, which is based on small sample estimates but incorporates a mixture-eliminating (ME) procedure to automatically assess the uniformity of the estimation windows. A statistical feature derived fro...
Year
DOI
Venue
2017
10.1109/TGRS.2017.2734064
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Polarimetric synthetic aperture radar,Synthetic aperture radar,Unsupervised learning,Data models,Scattering,Covariance matrices,Complexity theory
Sampling distribution,Data modeling,Pattern recognition,Test statistic,Synthetic aperture radar,Robustness (computer science),Artificial intelligence,Logarithm,Mathematics,Statistical hypothesis testing,Estimator
Journal
Volume
Issue
ISSN
55
12
0196-2892
Citations 
PageRank 
References 
0
0.34
9
Authors
5
Name
Order
Citations
PageRank
Dingsheng Hu111.38
Stian Normann Anfinsen225520.55
Xiaolan Qiu319026.75
Anthony Paul Doulgeris411611.14
Bin Lei5434.75