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Tensor-based Offset-Sparsity Decomposition for Hyperspectral Image Classification (CROSBI ID 659105)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Tian, Long ; Du, Qian ; Younan Nicolas ; Kopriva, Ivica Tensor-based Offset-Sparsity Decomposition for Hyperspectral Image Classification // Geoscience and Remote Sensing Symposium (IGARSS), 2017 IEEE International / Tjuatja, Saibun ; Kunkee, David (ur.). Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE), 2017. str. 3656-3659 doi: 10.1109/IGARSS.2017.8127791

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

Poveznice
Indeksiranost