http://iet.metastore.ingenta.com
1887

Fuzzy multiclass active learning for hyperspectral image classification

Fuzzy multiclass active learning for hyperspectral image classification

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios’ classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C-order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state-of-the-art AL methods are evaluated, such as breaking ties, margin sampling, and multi-class level uncertainty, with or without diversity criteria such as angle-based diversity (ABD), clustering-based diversity (CBD), and enhanced clustering-based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones.

References

    1. 1)
      • P. Olofsson , G.M. Foody , M. Herold .
        1. Olofsson, P., Foody, G.M., Herold, M., et al: ‘Good practices for estimating area and assessing accuracy of land change’, Remote Sens. Environ., 2014, 148, pp. 4257, doi: 10.1016/j.rse.2014.02.015.
        . Remote Sens. Environ. , 42 - 57
    2. 2)
      • G.M. Foody , A. Mathur .
        2. Foody, G.M., Mathur, A.: ‘Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification’, Remote Sens. Environ., 2004, 93, (1), pp. 107117, doi: 10.1016/j.rse.2004.06.017.
        . Remote Sens. Environ. , 1 , 107 - 117
    3. 3)
      • A. Samat , J. Li , S. Liu .
        3. Samat, A., Li, J., Liu, S., et al: ‘Improved hyperspectral image classification by active learning using pre-designed mixed pixels’, Pattern Recognit., 2016, 51, pp. 4358, doi: 10.1016/j.patcog.2015.08.019.
        . Pattern Recognit. , 43 - 58
    4. 4)
      • D. Tuia , F. Ratle , F. Pacifici .
        4. Tuia, D., Ratle, F., Pacifici, F., et al: ‘Active learning methods for remote sensing image classification’, IEEE Trans. Geosci. Remote Sens., 2009, 47, (7), pp. 22182232, doi: 10.1109/TGRS.2008.2010404.
        . IEEE Trans. Geosci. Remote Sens. , 7 , 2218 - 2232
    5. 5)
      • A. Samat , P. Gamba , P. Du .
        5. Samat, A., Gamba, P., Du, P., et al: ‘Active extreme learning machines for quad-polarimetric SAR imagery classification’, Int. J. Appl. Earth Obs. Geoinf., 2015, 35, pp. 305319, doi: 10.1016/j.jag.2014.09.019.
        . Int. J. Appl. Earth Obs. Geoinf. , 305 - 319
    6. 6)
      • A. Liu , G. Jun , J. Ghosh .
        6. Liu, A., Jun, G., Ghosh, J.: ‘A self-training approach to cost sensitive uncertainty sampling’, Mach. Learn., 2009, 76, (2–3), pp. 257270, doi: 10.1007/s10994-009-5131-9.
        . Mach. Learn. , 257 - 270
    7. 7)
      • B. Settles .
        7. Settles, B.: ‘Active learning literature survey’, University of Wisconsin, Madison, 2010, vol. 52, no. 55–66, p. 11.
        . , 11
    8. 8)
      • B. Demir , C. Persello , L. Bruzzone .
        8. Demir, B., Persello, C., Bruzzone, L.: ‘Batch-mode active-learning methods for the interactive classification of remote sensing images’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (3), pp. 10141031, doi: 10.1109/TGRS.2010.2072929.
        . IEEE Trans. Geosci. Remote Sens. , 3 , 1014 - 1031
    9. 9)
      • D. Tuia , M. Volpi , L. Copa .
        9. Tuia, D., Volpi, M., Copa, L., et al: ‘A survey of active learning algorithms for supervised remote sensing image classification’, IEEE J. Sel. Top. Signal Process., 2011, 5, (3), pp. 606617, doi: 10.1109/JSTSP.2011.2139193.
        . IEEE J. Sel. Top. Signal Process. , 3 , 606 - 617
    10. 10)
      • C. Persello , A. Boularias , M. Dalponte .
        10. Persello, C., Boularias, A., Dalponte, M., et al: ‘Cost-sensitive active learning with lookahead: optimizing field surveys for remote sensing data classification’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (10), pp. 66526664, doi: 10.1109/TGRS.2014.2300189.
        . IEEE Trans. Geosci. Remote Sens. , 10 , 6652 - 6664
    11. 11)
      • R. Wang , C.Y. Chow , S. Kwong .
        11. Wang, R., Chow, C.Y., Kwong, S.: ‘Ambiguity-based multiclass active learning’, IEEE Trans. Fuzzy Syst., 2015, 24, (1), pp. 242248, doi: 10.1109/TFUZZ.2015.2451698.
        . IEEE Trans. Fuzzy Syst. , 1 , 242 - 248
    12. 12)
      • P. Mitra , C.A. Murthy , S.K. Pal .
        12. Mitra, P., Murthy, C.A., Pal, S.K.: ‘A probabilistic active support vector learning algorithm’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (3), pp. 413418, doi: 10.1109/TPAMI.2004.1262340.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 413 - 418
    13. 13)
      • L. Mascarilla , M. Berthier , C. Frélicot .
        13. Mascarilla, L., Berthier, M., Frélicot, C.: ‘A k-order fuzzy or operator for pattern classification with k-order ambiguity rejection’, Fuzzy Sets Syst., 2008, 159, (15), pp. 20112029, doi: 10.1016/j.fss.2008.02.019.
        . Fuzzy Sets Syst. , 15 , 2011 - 2029
    14. 14)
      • T. Luo , K. Kramer , D.B. Goldgof .
        14. Luo, T., Kramer, K., Goldgof, D.B., et al: ‘Active learning to recognize multiple types of plankton’, J. Mach. Learn. Res., 2005, 6, pp. 589613.
        . J. Mach. Learn. Res. , 589 - 613
    15. 15)
      • K. Brinker .
        15. Brinker, K.: ‘Incorporating diversity in active learning with support vector machines’. Proc. 20th Int. Conf. on Machine Learning, Washington, DC, 2003, vol. 3, pp. 5966.
        . Proc. 20th Int. Conf. on Machine Learning , 59 - 66
    16. 16)
      • Z. Xu , K. Yu , V. Tresp .
        16. Xu, Z., Yu, K., Tresp, V., et al: ‘Representative sampling for text classification using support vector machines’. European Conf. on Information Retrieval, 2003, pp. 393407.
        . European Conf. on Information Retrieval , 393 - 407
    17. 17)
      • D. Dubois , L. Foulloy , G. Mauris .
        17. Dubois, D., Foulloy, L., Mauris, G., et al: ‘Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities’, Reliab. Comput., 2004, 10, (4), pp. 273297, doi: 10.1023/B:REOM.0000032115.22510.b5.
        . Reliab. Comput. , 4 , 273 - 297
    18. 18)
      • C. Frélicot , L. Mascarilla , A. Fruchard .
        18. Frélicot, C., Mascarilla, L., Fruchard, A.: ‘An ambiguity measure for pattern recognition problems using triangular-norms combination’, WSEAS Trans. Syst., 2004, 8, (3), pp. 27102715.
        . WSEAS Trans. Syst. , 3 , 2710 - 2715
    19. 19)
      • X.Z. Wang , L.C. Dong , J.H. Yan .
        19. Wang, X.Z., Dong, L.C., Yan, J.H.: ‘Maximum ambiguity-based sample selection in fuzzy decision tree induction’, IEEE Trans. Knowl. Data Eng., 2012, 24, (8), pp. 14911505, doi: 10.1109/TKDE.2011.67.
        . IEEE Trans. Knowl. Data Eng. , 8 , 1491 - 1505
    20. 20)
      • M.D. Mura , A. Villa , J.A. Benediktsson .
        20. Mura, M.D., Villa, A., Benediktsson, J.A., et al: ‘Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (3), pp. 542546, doi: 10.1109/LGRS.2010.2091253.
        . IEEE Geosci. Remote Sens. Lett. , 3 , 542 - 546
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0784
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0784
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address