NDLI: Regularised transfer learning for hyperspectral image classification

Content Provider	IET Digital Library
Author	Shi, Qian Zhang, Yipeng Liu, Xiaoping Zhao, Kefei
Abstract	This study presents a transfer learning method for addressing the insufficient sample problem in hyperspectral image classification. In order to find common feature representation for both the source domain and target domain, we introduce a regularisation based on Bregman divergence into the objective function of the subspace learning algorithm, which can minimise the Bregman divergence between the distribution of training samples in the source domain and the test samples in the target domain. Hyperspectral image with biased sampling is used to evaluate the effectiveness of the proposed method. The results show that the proposed method can achieve a higher classification accuracy than traditional subspace learning methods under the condition of biased sampling.
Starting Page	188
Ending Page	193
Page Count	6
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5145
Journal	IET Computer Vision
Publisher Date	2018-07-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biased Sampling Bregman Divergence Computer Vision And Image Processing Technique Earth Sciences Feature Extraction Feature Representation Geography And Cartography Computing Geophysical Image Processing Hyperspectral Image Hyperspectral Image Classification Image Classification Image Recognition Image Sampling Knowledge Engineering Technique Learning in AI Regularisation Regularised Transfer Learning Source Domain SubSpace Learning Algorithm Target Domain
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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