Tensor-based Offset-Sparsity Decomposition for Hyperspectral Image Classification (CROSBI ID 659105)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Tian, Long ; Du, Qian ; Younan Nicolas ; Kopriva, Ivica
engleski
Tensor-based Offset-Sparsity Decomposition for Hyperspectral Image Classification
In this paper, the tensor-based offset-sparsity decomposition (TOSD) method, or low-rank and sparse decomposition, is applied to hyperspectral imagery, where the low-rank tensor is considered to be enhanced or pruned data and used for classification. In the tensor form of dataset, all the information of the original 3D data cube, includes spatial and spectral information, can be better reserved. To make the low-rank assumption more possibly true, spatial and spectral segmentations are conducted in a preprocessing step for the TOSD. The experimental results demonstrate the TOSD offers better performance than the matrix-based one, and the spatial-spectral segmentation can further improve the performance.
Tensor ; low-rank recovery ; spatial-spectral segmentation ; classification ; hyperspectral imagery
Rad je rezultat zanstvene suradnje Ivice Koprive sa Instituta Ruđer Bošković i profesorice Qian Du sa Mississippi State University, SAD. Rad nije dio formalno prihvaćenog znanstvenog projekta.
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Podaci o prilogu
3656-3659.
2017.
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objavljeno
978-1-5090-4951-6
10.1109/IGARSS.2017.8127791
Podaci o matičnoj publikaciji
Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International
Tjuatja, Saibun ; Kunkee, David
Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE)
2153-7003
Podaci o skupu
Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International
poster
23.07.2017-28.07.2017
Fort Worth (TX), Sjedinjene Američke Države
Povezanost rada
Matematika, Računarstvo