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Unsupervised Estimation of the Equivalent Number of Looks in PolSAR Image with High Heterogeneity |
HU Dingsheng①②③ QIU Xiaolan①② Stian N. Anfinsen④ LEI Bin①② |
①(Institute of Electronics, Chinese Academy of Science, Beijing 100190, China)
②(Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China)
③(University of Chinese Academy of Science, Beijing 100049, China)
④(Department of Physics and Technology, UiT The Arctic University of Norway, Tromso 9037, Norway) |
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Abstract Equivalent Number of Looks (ENL) is an important parameter in statistical modelling of multi-look Polarimetric SAR (PolSAR) data. In some automated applications of PolSAR images, it is necessary to estimate the ENL in an unsupervised way without any manual intervention. The existing unsupervised estimation of ENL can not obtain accurate estimates for the images with high heterogeneity. To address this issue, a novel unsupervised estimation method is proposed here. It combines the mixture elimination and clustering based on texture, which reduces the effect of two main heterogeneity factors, mixture and texture. The validity of this method is evaluated with simulated and real data of different complexity.
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Received: 03 January 2017
Published: 14 June 2017
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Fund:The National Natural Science Foundation of China (61331017), The GF-3 High-Resolution Earth Observation System (30-Y20A12-9004-15/16, 03-Y20A11-9001-15/16) |
Corresponding Authors:
HU Dingsheng
E-mail: hds_iecas@hotmail.com
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