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

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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.

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Notes

  1. AVIRIS: Airborne Visible/Infrared Imaging Spectrometer.

  2. These two public data sets can be downloaded from: www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes.

References

  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):2282–2291

    Article  Google Scholar 

  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):447–459

    Article  Google Scholar 

  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):480–491

    Article  Google Scholar 

  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):1940–1949

    Article  Google Scholar 

  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):177–181

    Article  Google Scholar 

  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):3363–3373

    Article  Google Scholar 

  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):93–97

    Article  Google Scholar 

  8. Cevikalp H, Triggs B (2010) Face recognition based on image sets. In: CVPR, pp 2567–2573

    Google Scholar 

  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):1246–1259

  10. Du Q, Li W (2015) Kernel weighted joint collaborative representation for hyperspectral image classification. In: Proceedings of SPIE, vol 9501, pp 95010V1–95010V6

    Google Scholar 

  11. Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 6(4):325–327

    Article  Google Scholar 

  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):349–353

    Google Scholar 

  13. Gualtieri JA, Chettri SR, Cromp RF, Johnson LF (1999) Support vector machine classifiers as applied to aviris data. In: Airborne Earth Science Workshop

    Google Scholar 

  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):957–968

    Article  Google Scholar 

  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 8:37–47

    Article  Google Scholar 

  16. Lawrence RL, Wood SD, Sheley RL (2006) Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (randomforest). Remote Sens Environ 100(3):356–362

    Article  Google Scholar 

  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):4816–4829

    Article  Google Scholar 

  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):3707–3719

    Article  Google Scholar 

  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):25–36

    Article  Google Scholar 

  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):5923–5936

    Article  Google Scholar 

  21. Li S, Qi H (2011) Sparse representation based band selection for hyperspectral images. In: ICIP, pp 2693–2696

    Google Scholar 

  22. Li W, Du Q (2014) Joint within-class collaborative representation for hyperspectral image classification. IEEE J Sel Top Sign Proces 7(6):2200–2208

    Google Scholar 

  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):48–52

    Article  Google Scholar 

  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):389–393

    Article  Google Scholar 

  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 54:68–77

    Article  Google Scholar 

  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 Experience

  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):1320–1324

    Article  Google Scholar 

  28. Manolakis D, Shaw G (2002) Detection algorithms for hyperspectral imaging applications. IEEE Signal Proc Mag 19(1):29–43

    Article  Google Scholar 

  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):293–297

    Article  Google Scholar 

  30. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790

    Article  Google Scholar 

  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):1053–1061

    Article  MATH  Google Scholar 

  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):2734–2752

    Article  Google Scholar 

  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):5975–5991

    Article  Google Scholar 

  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):3747–3762

    Article  Google Scholar 

  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):542–546

    Article  Google Scholar 

  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 176–179

    Google Scholar 

  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 176–179

    Google Scholar 

  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):2401–2411

    Article  Google Scholar 

  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):309–320

    Article  Google Scholar 

  40. Plaza A, et al. (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122

    Article  Google Scholar 

  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):2276–2291

    Article  Google Scholar 

  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):527–541

    Article  Google Scholar 

  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):505–509

    Article  Google Scholar 

  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):58–69

    Article  Google Scholar 

  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):1235–1239

    Article  Google Scholar 

  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):736–740

    Article  Google Scholar 

  47. Vincent P, Bengio Y (2001) K-local hyperplane and convex distance nearest neighbor algorithms. In: NIPS, pp 985–992

    Google Scholar 

  48. Wang Z, Nasrabadi NM, Huang TS (2013) Discriminative and compact dictionary design for hyperspectral image classification using learning vq framework. In: ICASSP, pp 3427–3431

    Google Scholar 

  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):210–227

    Article  Google Scholar 

  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):1209–1213

    Article  Google Scholar 

  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):479–483

    Article  Google Scholar 

  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 952–956

    Google Scholar 

  53. Yi C, Nasrabadi NM, Tran TD (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49 (10):3973–3985

    Article  Google Scholar 

  54. Yi C, Nasrabadi NM, Tran TD (2011) Sparse representation for target detection in hyperspectral imagery. IEEE J Se Top Sign Proces 5(3):629–640

    Article  Google Scholar 

  55. Yi C, Nasrabadi NM, Tran TD (2013) Hyperspectral image classification via kernel sparse representation. IEEE Trans Geosci Remote Sens 51(1):217–231

    Article  Google Scholar 

  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):1397–1401

    Article  Google Scholar 

  57. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: Which helps face recognition? In: ICCV, pp 471–478

    Google Scholar 

  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):2170–2177

    Article  Google Scholar 

  59. Zou J, Li W, Du Q (2015) Sparse representation-based nearest neighbor classifiers for hyperspectral imagery. IEEE Geosci Remote Sens Lett 12(12):2418–2422

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China

    Chunjuan Bo, Huchuan Lu & Dong Wang

  2. College of Electromechanical Engineering, Dalian Minzu University, Dalian, 116600, China

    Chunjuan Bo

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  1. Chunjuan Bo
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  2. Huchuan Lu
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Correspondence to Dong Wang.

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Bo, C., Lu, H. & Wang, D. Spectral-spatial K-Nearest Neighbor approach for hyperspectral image classification. Multimed Tools Appl 77, 10419–10436 (2018). https://doi.org/10.1007/s11042-017-4403-9

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