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Volumn 2016-November, Issue , 2016, Pages 6890-6893

CRF learning with CNN features for hyperspectral image segmentation

Author keywords

Conditional Random Field; Convolutional Neural Network; Deep Learning; Image Segmentation; Superpixel

Indexed keywords


EID: 85007496775     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2016.7730798     Document Type: Conference Paper
Times cited : (45)

References (10)
  • 1
    • 85032751896 scopus 로고    scopus 로고
    • Hyperspectral image data analysis
    • Jan
    • D. Landgrebe, "Hyperspectral image data analysis," Signal Processing Magazine, IEEE, vol. 19, no. 1, pp. 17-28, Jan 2002
    • (2002) Signal Processing Magazine, IEEE , vol.19 , Issue.1 , pp. 17-28
    • Landgrebe, D.1
  • 5
    • 84872922940 scopus 로고    scopus 로고
    • Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning
    • J. Li, J. M. Bioucas-Dias, and A. Plaza, "Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 2, pp. 844-856, 2013
    • (2013) IEEE Transactions on Geoscience and Remote Sensing , vol.51 , Issue.2 , pp. 844-856
    • Li, J.1    Bioucas-Dias, J.M.2    Plaza, A.3
  • 8
    • 84946015334 scopus 로고    scopus 로고
    • SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery
    • X. Zhang, Selene E. Chew, Z. Xu, and N. D. Cahill, "SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery," Proc. SPIE, vol. 9472, no. S2, 2015
    • (2015) Proc. SPIE , vol.9472 , Issue.S2
    • Zhang, X.1    Chew, S.E.2    Xu, Z.3    Cahill, N.D.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.