Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding
No Thumbnail Available
Date
2014
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.
Description
Keywords
Sparse Coding, Hyperspectral Imagery, Anomaly Detection, Online Learning
Turkish CoHE Thesis Center URL
Fields of Science
Citation
Ulku, Irem; Toreyin, B. Ugur, "Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding", International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), (2014).
WoS Q
Scopus Q
Source
International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)