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Remote Sens. 2018, 10(8), 1307; https://doi.org/10.3390/rs10081307

A Generalized Zero-Shot Learning Framework for PolSAR Land Cover Classification

1
,
1,* , 1
,
1,2
and
1,2
1
School of Electronic Information, Wuhan University, Wuhan 430072, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 7 June 2018 / Revised: 26 July 2018 / Accepted: 17 August 2018 / Published: 19 August 2018
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Abstract

Most supervised classification methods for polarimetric synthetic aperture radar (PolSAR) data rely on abundant labeled samples, and cannot tackle the problem that categorizes or infers unseen land cover classes without training samples. Aiming to categorize instances from both seen and unseen classes simultaneously, a generalized zero-shot learning (GZSL)-based PolSAR land cover classification framework is proposed. The semantic attributes are first collected to describe characteristics of typical land cover types in PolSAR images, and semantic relevance between attributes is established to relate unseen and seen classes. Via latent embedding, the projection between mid-level polarimetric features and semantic attributes for each land cover class can be obtained during the training stage. The GZSL model for PolSAR data is constructed by mid-level polarimetric features, the projection relationship, and the semantic relevance. Finally, the labels of the test instances can be predicted, even for some unseen classes. Experiments on three real RadarSAT-2 PolSAR datasets show that the proposed framework can classify both seen and unseen land cover classes with limited kinds of training classes, which reduces the requirement for labeled samples. The classification accuracy of the unseen land cover class reaches about 73% if semantic relevance exists during the training stage. View Full-Text
Keywords: generalized zero-shot learning; semantic attributes; classification; polarimetric SAR; polarization feature generalized zero-shot learning; semantic attributes; classification; polarimetric SAR; polarization feature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gui, R.; Xu, X.; Wang, L.; Yang, R.; Pu, F. A Generalized Zero-Shot Learning Framework for PolSAR Land Cover Classification. Remote Sens. 2018, 10, 1307.

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