Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 9, pp 10419–10436 | Cite as

Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification

Article

Abstract

Hyperspectral image (HSI) classification is a very active research topic in remote sensing and has numerous potential applications. This paper presents a simple but effective classification method based on spectral-spatial information and K-nearest neighbor (KNN). To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all neighboring pixels of a given test pixel and training samples. In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By using two standard HSI benchmark, we evaluate the proposed method by comparing it with eight competing methods. Both qualitative and quantitative results demonstrate our SSKNN method achieves better performance than other ones.

Keywords

Hyperspectral image classification KNN Spectral-spatial Joint model 

Notes

Acknowledgments

This work was supported in part by the Natural Science Foundation of China under Grant No. 61502070, and in part by Fundamental Research Funds for Central Universities under Grant No. DUT16RC(4)16.

References

  1. 1.
    Banerjee A, Burlina P, Diehl C (2006) A support vector method for anomaly detection in hyperspectral imagery. IEEE Trans Geosci Remote Sens 44(8):2282–2291Google Scholar
  2. 2.
    Bannari A, Pacheco A, Staenz K, McNairn H, Omari K (2006) Estimating and mapping crop residues cover on agricultural lands using hyperspectral and ikonos data. Remote Sens Environ 104(4):447–459Google Scholar
  3. 3.
    Benediktsson JA, Palmason JA, Sveinsson JR (2005) Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans Geosci Remote Sens 43(3):480–491Google Scholar
  4. 4.
    Benediktsson JA, Pesaresi M, Amason K (2003) Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans Geosci Remote Sens 41(9):1940–1949Google Scholar
  5. 5.
    Bo C, Lu H, Wang D (2016) Hyperspectral image classification via JCR and SVM models with decision fusion. IEEE Geosci Remote Sens Lett 13(2):177–181Google Scholar
  6. 6.
    Bruzzone L, Chi M, Marconcini M (2006) A novel transductive svm for semisupervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 44(11):3363–3373Google Scholar
  7. 7.
    Camps-Valls G, Gomez-Chova L, Muñoz-Marí J, Vila-Francés J, Calpe-Maravilla J (2006) Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett 3(1):93–97Google Scholar
  8. 8.
    Cevikalp H, Triggs B (2010) Face recognition based on image sets. In: CVPR, pp 2567–2573Google Scholar
  9. 9.
    Datt B, McVicar TR, Van Niel TG, Jupp DLB, Pearlman JS (2003) Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural indexes. IEEE Trans Geosci Remote Sens 41(6):1246–1259Google Scholar
  10. 10.
    Du Q, Li W (2015) Kernel weighted joint collaborative representation for hyperspectral image classification. In: Proceedings of SPIE, vol 9501, pp 95010V1–95010V6Google Scholar
  11. 11.
    Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 6(4):325–327Google Scholar
  12. 12.
    Gao L, Li J, Khodadadzadeh M, Plaza A, Zhang B, He Z, Yan H (2014) Subspace-based support vector machines for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(2):349–353Google Scholar
  13. 13.
    Gualtieri JA, Chettri SR, Cromp RF, Johnson LF (1999) Support vector machine classifiers as applied to aviris data. In: Airborne Earth Science WorkshopGoogle Scholar
  14. 14.
    Krishnapuram B, Carin L, Figueiredo MAT, Hartemink AJ (2005) Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Trans Pattern Anal Mach Intell 27(6):957–968Google Scholar
  15. 15.
    Larsolle A, Muhammed HH (2007) Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precis Agric 8:37–47Google Scholar
  16. 16.
    Lawrence RL, Wood SD, Sheley RL (2006) Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sens Environ 100(3):356–362Google Scholar
  17. 17.
    Li J, Marpu PR, Plaza A, Bioucas-Dias JM, Benediktsson JA (2013) Generalized composite kernel framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens 51(9):4816–4829Google Scholar
  18. 18.
    Li J, Zhang H, Huang Y, Zhang L (2014) Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary. IEEE Trans Geosci Remote Sens 52(6):3707–3719Google Scholar
  19. 19.
    Li J, Zhang H, Zhang L (2014) Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification. ISPRS J Photogramm Remote Sens 94(8):25–36Google Scholar
  20. 20.
    Li J, Zhang H, Zhang L, Huang X, Zhang L (2014) Joint collaborative representation with multitask learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 52(9):5923–5936Google Scholar
  21. 21.
    Li S, Qi H (2011) Sparse representation based band selection for hyperspectral images. In: ICIP, pp 2693–2696Google Scholar
  22. 22.
    Li W, Du Q (2014) Joint within-class collaborative representation for hyperspectral image classification. IEEE J Sel Top Sign Proces 7(6):2200–2208Google Scholar
  23. 23.
    Li W, Du Q, Xiong M (2015) Kernel collaborative representation with tikhonov regularization for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12 (1):48–52Google Scholar
  24. 24.
    Li W, Du Q, Zhang F, Hu W (2015) Collaborative-representation-based nearest neighbor classifier for hyperspectral imagery. IEEE Geosci Remote Sens Lett 12(2):389–393Google Scholar
  25. 25.
    Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77Google Scholar
  26. 26.
    Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation: Practice and ExperienceGoogle Scholar
  27. 27.
    Liu J, Wu Z, Sun L, Wei Z, Xiao L (2014) Hyperspectral image classification using kernel sparse representation and semilocal spatial graph regularization. IEEE Geosci Remote Sens Lett 11(8):1320–1324Google Scholar
  28. 28.
    Manolakis D, Shaw G (2002) Detection algorithms for hyperspectral imaging applications. IEEE Signal Proc Mag 19(1):29–43Google Scholar
  29. 29.
    Marpu PR, Pedergnana M, Mura MD, Benediktsson JA, Bruzzone L (2013) Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci Remote Sens Lett 10(2):293–297Google Scholar
  30. 30.
    Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790Google Scholar
  31. 31.
    Melgani F, Serpico SB (2002) A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing images. Pattern Recogn Lett 23(9):1053–1061MATHGoogle Scholar
  32. 32.
    Moser G, Serpico SB (2013) Combining support vector machines and markov random fields in an integrated framework for contextual image classification. IEEE Trans Geosci Remote Sens 51(5):2734–2752Google Scholar
  33. 33.
    Mura MD, Benediktsson JA, Waske B, Bruzzone L (2010) Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int J Remote Sens 31(22):5975–5991Google Scholar
  34. 34.
    Mura MD, Benediktsson JA, Waske B, Bruzzone L (2010) Morphological attribute profiles for the analysis of very high resolution images. IEEE Trans Geosci Remote Sens 48(10):3747–3762Google Scholar
  35. 35.
    Mura MD, Villa A, Benediktsson JA, Chanussot J, Bruzzone L (2011) Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geosci Remote Sens Lett 8(3):542–546Google Scholar
  36. 36.
    Palmason JA, Benediktsson JA, Sveinsson JR, Chanussot J (2005) Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis In: IGARSS, pp 176–179Google Scholar
  37. 37.
    Palmason JA, Benediktsson JA, Sveinsson JR, Chanussot J (2005) Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis. In: IGARSS, pp 176–179Google Scholar
  38. 38.
    Patel N, Patnaik C, Dutta S, Shekh A, Dave A (2001) Study of crop growth parameters using airborne imaging spectrometer data. Int J Remote Sens 22 (12):2401–2411Google Scholar
  39. 39.
    Pesaresi M, Benediktsson JA (2001) A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans Geosci Remote Sens 39 (2):309–320Google Scholar
  40. 40.
    Plaza A, et al. (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122Google Scholar
  41. 41.
    Qian Y, Ye M, Zhou J (2013) Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans Geosci Remote Sens 51(4):2276–2291Google Scholar
  42. 42.
    Soltani-Farani A-A, Rabiee HR, Hosseini SA (2015) Spatial-aware dictionary learning for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53 (1):527–541Google Scholar
  43. 43.
    Srinivas U, Yi C, Monga V, Nasrabadi NM, Tran TD (2013) Exploiting sparsity in hyperspectral image classification via graphical models. IEEE Geosci Remote Sens Lett 10(3):505–509Google Scholar
  44. 44.
    Stein DWJ, Beaven SG, Hoff LE, Winter EM, Schaum AP, Stocker AD (2002) Anomaly detection from hyperspectral imagery. IEEE Signal Proc Mag 19 (1):58–69Google Scholar
  45. 45.
    Sun X, Qu Q, Nasrabadi NM, Tran TD (2014) Structured priors for sparse-representation-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 11(7):1235–1239Google Scholar
  46. 46.
    Tarabalka Y, Fauvel M, Chanussot J, Benediktsson JA (2010) Svm-and mrf-based method for accurate classification of hyperspectral images. IEEE Geosci Remote Sens Lett 7(4):736–740Google Scholar
  47. 47.
    Vincent P, Bengio Y (2001) K-local hyperplane and convex distance nearest neighbor algorithms. In: NIPS, pp 985–992Google Scholar
  48. 48.
    Wang Z, Nasrabadi NM, Huang TS (2013) Discriminative and compact dictionary design for hyperspectral image classification using learning vq framework. In: ICASSP, pp 3427–3431Google Scholar
  49. 49.
    Wright J, Yang AY, Ganesh A, Sastry SS, Yi M (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31 (2):210–227Google Scholar
  50. 50.
    Xiong M, Ran Q, Li W, Zou J, Du Q (2015) Hyperspectral image classification using weighted joint collaborative representation. IEEE Geosci Remote Sens Lett 12(6):1209–1213Google Scholar
  51. 51.
    Yang S, Jin H, Wang M, Ren Yu, Jiao L (2014) Data-driven compressive sampling and learning sparse coding for hyperspectral image classification. IEEE Geosci Remote Sens Lett 11(2):479–483Google Scholar
  52. 52.
    Yang S, Lu H, Li Y, Serikawa S (2013) Proposal of a multi-frame images fusion model on dual tree complex wavelet transform domain. In: ICMLC, pp 952–956Google Scholar
  53. 53.
    Yi C, Nasrabadi NM, Tran TD (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49 (10):3973–3985Google Scholar
  54. 54.
    Yi C, Nasrabadi NM, Tran TD (2011) Sparse representation for target detection in hyperspectral imagery. IEEE J Se Top Sign Proces 5(3):629–640Google Scholar
  55. 55.
    Yi C, Nasrabadi NM, Tran TD (2013) Hyperspectral image classification via kernel sparse representation. IEEE Trans Geosci Remote Sens 51(1):217–231Google Scholar
  56. 56.
    Zhang E, Zhang X, Liu H, Jiao L (2015) Fast multifeature joint sparse representation for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(7):1397–1401Google Scholar
  57. 57.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: Which helps face recognition? In: ICCV, pp 471–478Google Scholar
  58. 58.
    Zomer R, Trabucco A, Ustin S (2009) Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. J Environ Manag 90(7):2170–2177Google Scholar
  59. 59.
    Zou J, Li W, Du Q (2015) Sparse representation-based nearest neighbor classifiers for hyperspectral imagery. IEEE Geosci Remote Sens Lett 12(12):2418–2422Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina
  2. 2.College of Electromechanical EngineeringDalian Minzu UniversityDalianChina

Personalised recommendations