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

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

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 class...

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Bibliographic Details
Main Authors: Rong Gui, Xin Xu, Lei Wang, Rui Yang, Fangling Pu
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Remote Sensing
Subjects:
generalized zero-shot learning
semantic attributes
classification
polarimetric SAR
polarization feature
Online Access:http://www.mdpi.com/2072-4292/10/8/1307

Internet

http://www.mdpi.com/2072-4292/10/8/1307

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