Pretraga

Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 983810

Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery

Tian, Long; Du, Qian; Kopriva, Ivica; Younan, Nicolas
Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery // IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium / Plaza, Antonio ; Jimenez, Juan Antonio (ur.).
Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE), 2018. str. 8488-8491 doi:10.1109/IGARSS.2018.8519284 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

CROSBI ID: 983810 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery

Autori
Tian, Long ; Du, Qian ; Kopriva, Ivica ; Younan, Nicolas

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium / Plaza, Antonio ; Jimenez, Juan Antonio - Piscataway (NJ) : Institute of Electrical and Electronics Engineers (IEEE), 2018, 8488-8491

ISBN
978-1-5386-7150-4

Skup
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018)

Mjesto i datum
Valencia, Španjolska, 22.07.2018. - 27.07.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Hyperspectral image ; clustering ; low-rank sparse subspace clustering ; multi-view learning ; spatial-spectral feature

Sažetak
Hyperspectral image (HSI) Clustering is an unsupervised task, which segments pixels into different groups without using labeled samples. In this paper, spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC)algorithm is proposed. Due to significant number of spectra bands HSI contains much more information than a regular image. These spectral information can be considered as multiview. In this paper, the spectral partitioning is applied to generate spectral views which contain correlated bands. Morphological features of the original HSI are taken as another view which contains spatial features. Principal components construct another view, which eliminates the noise in the original dataset. After the multi-view dataset is formed, multi-view low-rank sparse subspace clustering is applied to segment HSI. Our experiments show that the performance of the proposed SSMLC is better than other that of clustering algorithms such as sparse subspace clustering and low-rank sparse subspace clustering.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA

Projekti:
HRZZ-IP-2016-06-5235 - Strukturne dekompozicije empirijskih podataka za računalno potpomognutu dijagnostiku bolesti (DEDAD) (Kopriva, Ivica, HRZZ - 2016-06) ( CroRIS)

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Ivica Kopriva (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi doi.org ieeexplore.ieee.org

Citiraj ovu publikaciju:

Tian, Long; Du, Qian; Kopriva, Ivica; Younan, Nicolas
Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery // IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium / Plaza, Antonio ; Jimenez, Juan Antonio (ur.).
Piscataway (NJ): Institute of Electrical and Electronics Engineers (IEEE), 2018. str. 8488-8491 doi:10.1109/IGARSS.2018.8519284 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Tian, L., Du, Q., Kopriva, I. & Younan, N. (2018) Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery. U: Plaza, A. & Jimenez, J. (ur.)IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium doi:10.1109/IGARSS.2018.8519284.
@article{article, author = {Tian, Long and Du, Qian and Kopriva, Ivica and Younan, Nicolas}, year = {2018}, pages = {8488-8491}, DOI = {10.1109/IGARSS.2018.8519284}, keywords = {Hyperspectral image, clustering, low-rank sparse subspace clustering, multi-view learning, spatial-spectral feature}, doi = {10.1109/IGARSS.2018.8519284}, isbn = {978-1-5386-7150-4}, title = {Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery}, keyword = {Hyperspectral image, clustering, low-rank sparse subspace clustering, multi-view learning, spatial-spectral feature}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Valencia, \v{S}panjolska} }
@article{article, author = {Tian, Long and Du, Qian and Kopriva, Ivica and Younan, Nicolas}, year = {2018}, pages = {8488-8491}, DOI = {10.1109/IGARSS.2018.8519284}, keywords = {Hyperspectral image, clustering, low-rank sparse subspace clustering, multi-view learning, spatial-spectral feature}, doi = {10.1109/IGARSS.2018.8519284}, isbn = {978-1-5386-7150-4}, title = {Spatial-spectral Based Multi-view Low-rank Sparse Sbuspace Clustering for Hyperspectral Imagery}, keyword = {Hyperspectral image, clustering, low-rank sparse subspace clustering, multi-view learning, spatial-spectral feature}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Valencia, \v{S}panjolska} }

Citati:

    Altmetrijski pokazatelji:


    Podijeli:





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font
    CROSBI koristi kolačiće (cookies) kako bi poboljšao funkcionalnost stranice.