Regularised transfer learning for hyperspectral image classification

Autor:	Qian Shi, Yipeng Zhang, Xiaoping Liu, Kefei Zhao
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	hyperspectral image classification source domain target domain regularisation Bregman divergence subspace learning algorithm Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Zdroj:	IET Computer Vision, Vol 13, Iss 2, Pp 188-193 (2019)
Druh dokumentu:	article
ISSN:	1751-9640 1751-9632
DOI:	10.1049/iet-cvi.2018.5145
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/43048482486f475ab3c44d22c026df19 Zobrazit plný text záznamu Plný text View record in DOAJ