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access icon free Robust semi-NMF with total variation for unsupervised SAR image change detection

  • Author(s): Heng-Chao Li 1 ; Qing-He Zhao 1 ; Gang Yang 1 ; Kun Fu 2 ; William J. Emery 3
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  • Source: Volume 54, Issue 14, 12 July 2018, p. 892 – 894
    DOI:  10.1049/el.2018.1269 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Received 15/04/2018, Published 06/06/2018
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A novel unsupervised change detection for synthetic aperture radar (SAR) images using robust semi-non-negative matrix factorisation (NMF) with total variation (TV) is proposed. In order to effectively handle the noise and outliers as well as consider spatial contextual information, correntropy induced metric and TV regularisation are simultaneously integrated into semi-NMF to build a robust semi-NMF with TV model. Subsequently, the update solutions of the model are optimised by the Karush–Kuhn–Tucker condition and fast gradient projection algorithm. The experimental results demonstrate the effectiveness of the proposed method both visually and quantitatively.

Inspec keywords: unsupervised learning; matrix decomposition; gradient methods; radar imaging; radar detection; radar computing

Other keywords: spatial contextual information; TV regularisation; Karush-Kuhn-Tucker condition; synthetic aperture radar images; robust seminonnegative matrix factorisation with total variation; robust semiNMF with TV model; correntropy induced metric; fast gradient projection algorithm; unsupervised SAR image change detection

Subjects: Radar equipment, systems and applications; Algebra; Optical, image and video signal processing; Signal detection; Knowledge engineering techniques; Algebra; Computer vision and image processing techniques; Electrical engineering computing; Optimisation techniques; Optimisation techniques

References

    1. 1)
    2. 2)
      • 6. Chan, T., Esedoglu, S., Park, F., et al: ‘Recent developments in total variation image restoration’, in Paragios, N., Chen, Y., Faugeras, O.D. (Eds.): ‘Handbook of mathematical models in computer vision’ (Springer US, New York, NY, USA, 2005), pp. 17–30.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 2. Mirzal, A., Furukawa, M.: ‘On the clustering aspect of nonnegative matrix factorization’. IEEE Int. Conf. Electronics and Information Engineering (ICEIE), Kyoto, Japan, August 2010, pp. 405–408.
    7. 7)
      • 4. Li, H.C., Longbotham, N., Emery, W.J.: ‘Unsupervised change detection of remote sensing images based on semi-nonnegative matrix factorization’. IEEE Int. Conf. Geoscience and Remote Sensing Symp. (IGARSS), Quebec City, Canada, July 2014, pp. 1289–1292.
    8. 8)
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