Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Selecting optimal bands for sub-pixel target detection in hyperspectral images based on implanting synthetic targets

© The Institution of Engineering and Technology
Received 19/04/2018, Accepted 08/10/2018, Revised 17/09/2018, Published 15/10/2018

Target detection at sub-pixel abundances is, in fact, one of the challenging issues of hyperspectral image processing. Selection of optimal bands to improve sub-pixel target detection (STD) performance is one of the common solutions, applied by many researchers. Nevertheless, the absence of sufficient training data is the main weakness of selecting optimal bands with regard to this approach. The present research introduces a new band selection method for STD in hyperspectral images, based on creating training data, in which the desired target spectrum is implanted randomly in a series of host pixels from the entire hyperspectral image. Afterwards, via running an optimisation algorithm twice, with the aim of minimising the false alarm rate (FAR) in local adaptive coherence estimator target detection algorithm, the number of optimal bands and optimal spectral bands are selected. In this study, the performance of three optimisation methods including the genetic algorithm (GA), Grey Wolf optimisation (GWO), and particle swarm optimisation (PSO) are compared. Experimental results on HyMap and Hyperion datasets show that the proposed method obtains the minimum FAR compared with the rest of the evaluated methods. Also, based on the results obtained, GWO outperforms GA and PSO optimisation methods in the STD domain.

Inspec keywords: object detection; particle swarm optimisation; genetic algorithms; hyperspectral imaging; statistical analysis; learning (artificial intelligence); geophysical image processing; spectral analysis

Other keywords: hyperspectral images; optimal spectral bands; local adaptive coherence estimator target detection algorithm; target spectrum; synthetic targets; genetic algorithm; sub-pixel target detection performance; optimal bands; hyperspectral image processing; grey wolf optimisation; training data; STD domain; sub-pixel abundances; particle swarm optimisation; band selection method

Subjects: Optimisation techniques; Optimisation techniques; Optical, image and video signal processing; Other topics in statistics; Computer vision and image processing techniques; Other topics in statistics

References

    1. 1)
      • 23. Stefanou, M.S., Kerekes, J.P.: ‘A method for assessing spectral image utility’, IEEE Trans. Geosci. Remote Sens., 2009, 47, pp. 1698–1706.
    2. 2)
      • 25. Basener, W.F., Nance, E., Kerekes, J.: ‘The target implant method for predicting target difficulty and detector performance in hyperspectral imagery’. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 2011, p. 80481H.
    3. 3)
      • 1. Geng, X., Ji, L., Sun, K., et al: ‘CEM: more bands, better performance’, IEEE Geosci. Remote Sens. Lett., 2014, 11, pp. 1876–1880.
    4. 4)
      • 4. Kim, B., Landgrebe, D.A.: ‘Hierarchical classifier design in high-dimensional numerous class cases’, IEEE Trans. Geosci. Remote Sens., 1991, 29, pp. 518–528.
    5. 5)
      • 22. Khazai, S., Homayouni, S., Safari, A., et al: ‘Anomaly detection in hyperspectral images based on an adaptive support vector method’, IEEE Geosci. Remote Sens. Lett., 2011, 8, pp. 646–650.
    6. 6)
      • 27. Yang, H., Du, Q., Chen, G.: ‘Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2012, 5, pp. 544–554.
    7. 7)
      • 16. Sun, K., Geng, X., Ji, L.: ‘A new sparsity-based band selection method for target detection of hyperspectral image’, IEEE Geosci. Remote Sens. Lett., 2015, 12, pp. 329–333.
    8. 8)
      • 38. Khazai, S., Safari, A., Mojaradi, B., et al: ‘An approach for subpixel anomaly detection in hyperspectral images’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2013, 6, pp. 769–778.
    9. 9)
      • 17. Li, S., Wu, H., Wan, D., et al: ‘An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine’, Knowl.-Based Syst., 2011, 24, pp. 40–48.
    10. 10)
      • 24. Zhang, L., Zhang, L., Tao, D., et al: ‘Hyperspectral remote sensing image subpixel target detection based on supervised metric learning’, IEEE Trans. Geosci. Remote Sens., 2014, 52, pp. 4955–4965.
    11. 11)
      • 29. Emary, E., Zawbaa, H.M., Hassanien, A.E.: ‘Binary Grey Wolf optimization approaches for feature selection’, Neurocomputing, 2016, 172, pp. 371–381.
    12. 12)
      • 8. Chang, C.-I., Wang, S.: ‘Constrained band selection for hyperspectral imagery’, IEEE Trans. Geosci. Remote Sens., 2006, 44, pp. 1575–1585.
    13. 13)
      • 11. Zhang, J., Cao, Y., Zhuo, L., et al: ‘Improved band similarity-based hyperspectral imagery band selection for target detection’, J. Appl. Remote Sens., 2015, 9, p. 095091.
    14. 14)
      • 35. Rosario, D.S.: ‘Algorithm development for hyperspectral anomaly detection’, University of Maryland, College Park, 2008.
    15. 15)
      • 19. Chang, C.-I., Du, Q., Sun, T.-L., et al: ‘A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification’, IEEE Trans. Geosci. Remote Sens., 1999, 37, pp. 2631–2641.
    16. 16)
      • 10. Geng, X., Sun, K., Ji, L.: ‘Band selection for target detection in hyperspectral imagery using sparse CEM’, Remote Sens. Lett., 2014, 5, pp. 1022–1031.
    17. 17)
      • 36. Snyder, D., Kerekes, J., Fairweather, I., et al: ‘Development of a web-based application to evaluate target finding algorithms’. IEEE Int. Geoscience and Remote Sensing Symp. 2008 IGARSS 2008, 2008, pp. II-915–II-918.
    18. 18)
      • 21. Kuo, B.-C., Ho, H.-H., Li, C.-H., et al: ‘A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, pp. 317–326.
    19. 19)
      • 15. Cao, Y., Zhang, J., Zhuo, L., et al: ‘An unsupervised band selection based on band similarity for hyperspectral image target detection’. Proc. Int. Conf. Internet Multimedia Computing and Service, 2014, p. 336.
    20. 20)
      • 6. Sharifi Hashjin, S., Darvishi Boloorani, A., Khazai, S.: ‘A band selection method for sub-pixel target detection in hyperspectral images’, J. Geomatics Sci. Technol., 2016, 6, pp. 129–139.
    21. 21)
      • 2. Du, Q., Bioucas-Dias, J.M., Plaza, A.: ‘Hyperspectral band selection using a collaborative sparse model’. 2012 IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), 2012, pp. 3054–3057.
    22. 22)
      • 12. Balasubramanian, G., Shettigara, V., Angeli, S., et al: ‘Band selection using support vector machines for improving target detection in hyperspectral images’. Ninth Biennial Conf. Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, 2007, pp. 446–453.
    23. 23)
      • 26. Gholizadeh, H., Zoej, M.J.V., Mojaradi, B.: ‘Impact of informative band selection on target detection performance’. Proc. SPIE, 2011, p. 81801C.
    24. 24)
      • 28. Su, H., Du, Q., Chen, G., et al: ‘Optimized hyperspectral band selection using particle swarm optimization’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, pp. 2659–2670.
    25. 25)
      • 37. Neuenschwander, A.L., Crawford, M.M., Ringrose, S.: ‘Results from the EO-1 experiment — a comparative study of earth observing-1 advanced land imager (ALI) and landsat ETM + data for land cover mapping in the Okavango Delta, Botswana’, Int. J. Remote Sens., 2005, 26, pp. 4321–4337.
    26. 26)
      • 7. Keshava, N.: ‘Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries’, IEEE Trans. Geosci. Remote Sens., 2004, 42, pp. 1552–1565.
    27. 27)
      • 20. Archibald, R., Fann, G.: ‘Feature selection and classification of hyperspectral images with support vector machines’, IEEE Geosci. Remote Sens. Lett., 2007, 4, pp. 674–677.
    28. 28)
      • 32. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey Wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 46–61.
    29. 29)
      • 30. Cohen, Y., August, Y., Blumberg, D.G., et al: ‘Evaluating subpixel target detection algorithms in hyperspectral imagery’, J. Electr. Comput. Eng., 2012, 2012, p. 2.
    30. 30)
      • 5. Xu, Y., Du, Q., Younan, N.H.: ‘Particle swarm optimization-based band selection for hyperspectral target detection’, IEEE Geosci. Remote Sens. Lett., 2017, 14, pp. 554–558.
    31. 31)
      • 14. Minet, J., Taboury, J., Goudail, F., et al: ‘Influence of band selection and target estimation error on the performance of the matched filter in hyperspectral imaging’, Appl. Opt., 2011, 50, pp. 4276–4285.
    32. 32)
      • 31. Manolakis, D., Marden, D., Shaw, G.A.: ‘Hyperspectral image processing for automatic target detection applications’, Linc. Lab. J., 2003, 14, pp. 79–116.
    33. 33)
      • 3. Bernard, K., Tarabalka, Y., Angulo, J., et al: ‘Spectral–spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach’, IEEE Trans. Image Process., 2012, 21, pp. 2008–2021.
    34. 34)
      • 34. Bioucas-Dias, J.M., Nascimento, J.M.: ‘Hyperspectral subspace identification’, IEEE Trans. Geosci. Remote Sens., 2008, 46, pp. 2435–2445.
    35. 35)
      • 39. Saipullah, K.M., Kim, D.-H.: ‘Target detection of hyperspectral images based on their Fourier spectral features’, Opt. Eng., 2012, 51, p. 111704.
    36. 36)
      • 13. Diani, M., Acito, N., Greco, M., et al: ‘A new band selection strategy for target detection in hyperspectral images’. Knowledge-Based Intelligent Information and Engineering Systems, 2008, pp. 424–431.
    37. 37)
      • 33. Eberhart, R., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. Sixth Int. Symp. Micro Machine and Human Science, 1995 MHS'95, 1995, pp. 39–43.
    38. 38)
      • 18. Du, Q., Yang, H.: ‘Similarity-based unsupervised band selection for hyperspectral image analysis’, IEEE Geosci. Remote Sens. Lett., 2008, 5, pp. 564–568.
    39. 39)
      • 9. Wang, Y., Huang, S., Liu, D., et al: ‘A new band removed selection method for target detection in hyperspectral image’, J. Opt., 2013, 42, pp. 208–213.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5324
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5324
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address