Soft Computing

, Volume 22, Issue 5, pp 1475–1490 | Cite as

Segmentation and classification of hyperspectral images using CHV pattern extraction grid

  • Gokulakrishnan Gopalan
  • Tholkappia Arasu Govindarajan
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  • 54 Downloads

Abstract

Recently, most critical challenge tasks in remote sensing applications are the segmentation and classification of hyperspectral images (HSI). Data dependency and relation among the frequency bands are the major drawbacks in the traditional HSI segmentation and classification techniques for large-size images. To segment and classify the regions in HSI, the clear description of the edge information is necessary. The presence of noise needs to be removed prior to the clear edge information extraction. To alleviate such issues, the new way of pattern extraction is proposed in this paper. Initially, the fuzzy-based adaptive median filtering removes the noise present in the image which is the prior step for clear information extraction. The integration of circular local binary pattern with the sorted local horizontal vector (CHV) patterns extracts the clear edge information necessary for classification. The intensity values from the color-based feature extraction and the CHV pattern are used to construct the relevance vector machine kernel with the limits. The comparative analysis between the proposed CHV-based pattern extraction grid with the existing techniques regarding the various metrics such as accuracy, sensitivity, specificity, rate variations and coefficient variations assures the effectiveness of proposed work in remote sensing applications.

Keywords

Hyperspectral image (HSI) Color-based features Fuzzy-based adaptive median filter (FAMF) Circular local binary pattern (CLBP) Sorted local horizontal–vertical (SLHV) Relevance vector machine (RVM) 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Akbari D, Homayouni S, Safari A, Khazai S (2014) An efficient framework for spectral–spatial classification of hyperspectral images in urban areas. In: IEEE international geoscience and remote sensing symposium (IGARSS) 2014: 2886–2889Google Scholar
  2. Bor-Chen K, Hsin-Hua H, Cheng-Hsuan L, Chih-Cheng H, Jin-Shiuh T (2014) A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7:317–326CrossRefGoogle Scholar
  3. Chunsen Z, Yiwei Z, Chenyi F (2016) Spectral–spatial classification of hyperspectral images using probabilistic weighted strategy for multifeature fusion. IEEE Geosci Remote Sens Lett 13:1562–1566CrossRefGoogle Scholar
  4. Dópido I, Villa A, Plaza A, Gamba P (2012) A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 5:421–435CrossRefGoogle Scholar
  5. Fauvel M, Tarabalka Y, Benediktsson JA, Chanussot J, Tilton JC (2013) Advances in spectral–spatial classification of hyperspectral images. Proc IEEE 101:652–675CrossRefGoogle Scholar
  6. Gao Y, Ji R, Cui P, Dai Q, Hua G (2014) Hyperspectral image classification through bilayer graph-based learning. IEEE Trans Image Process 23:2769–2778MathSciNetCrossRefMATHGoogle Scholar
  7. Ghamisi P, Couceiro MS, Martins FM, Benediktsson J Atli (2014) Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52:2382–2394CrossRefGoogle Scholar
  8. Gu B, Sheng VS (2017) A robust regularization path algorithm for \(\nu \)-support vector classification. IEEE Trans Neural Netw Learn Syst 28:1241–1248CrossRefGoogle Scholar
  9. Gu B, Sheng VS, Li S (2015a) Bi-parameter space partition for cost-sensitive SVM. In: IJCAI, pp 3532–3539Google Scholar
  10. Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015b) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150CrossRefGoogle Scholar
  11. Gu B, Sun X, Sheng VS (2017) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst 28:1647–1656MathSciNetGoogle Scholar
  12. Hyperspectral remote sensing scenes (2014). Available: http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes
  13. Ji R, Gao Y, Hong R, Liu Q, Tao D, Li X (2014) Spectral–spatial constraint hyperspectral image classification. IEEE Trans Geosci Remote Sens 52:1811–1824CrossRefGoogle Scholar
  14. Kang X, Li S, Benediktsson JA (2014) Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans Geosci Remote Sens 52:2666–2677CrossRefGoogle Scholar
  15. Li J, Bioucas-Dias JM, Plaza A (2010) Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans Geosci Remote Sens 48:4085–4098Google Scholar
  16. Li J, Marpu P Reddy, Plaza A, Bioucas-Dias JM, Benediktsson J Atli (2013) Generalized composite kernel framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens 51:4816–4829CrossRefGoogle Scholar
  17. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10:507–518CrossRefGoogle Scholar
  18. Lunga D, Prasad S, Crawford MM, Ersoy O (2014) Manifold-learning-based feature extraction for classification of hyperspectral data: a review of advances in manifold learning. IEEE Signal Process Mag 31:55–66CrossRefGoogle Scholar
  19. 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 10:293–297CrossRefGoogle Scholar
  20. Meka A, Chaudhuri S (2015) A technique for simultaneous visualization and segmentation of hyperspectral data. IEEE Trans Geosci Remote Sens 53:1707–1717CrossRefGoogle Scholar
  21. Menon V, Prasad S, Fowler JE (2015) Hyperspectral classification using a composite kernel driven by nearest-neighbor spatial features. In: IEEE international conference on image processing (ICIP), pp 2100–2104Google Scholar
  22. Rafi M, Shaikh MS (2013) A comparison of SVM and RVM for document classification. arXiv preprint arXiv:1301.2785
  23. Salmon BP, Kleynhans W, van den Bergh F, Olivier JC, Marais WJ, Grobler TL et al (2012) A search algorithm to meta-optimize the parameters for an extended Kalman filter to improve classification on hyper-temporal images. In: 2012 IEEE international geoscience and remote sensing symposium (IGARSS), pp 4974–4977Google Scholar
  24. Tao F, Peng Y (2014) A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique. J Food Eng 126:98–106CrossRefGoogle Scholar
  25. Tarabalka Y, Chanussot J, Benediktsson JA (2010) Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit 43:2367–2379CrossRefMATHGoogle Scholar
  26. VeeraSenthilKumar G, Dhivya M, Sivasangari R, Suganya V (2014) Fuzzy based hyperspectral image segmentation using sub-pixel detection. Int J Inf 4:179–188Google Scholar
  27. Vidal M, Amigo JM (2012) Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom Intell Lab Syst 117:138–148CrossRefGoogle Scholar
  28. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl. doi: 10.1007/s11042-016-4153-0
  29. Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  30. Yin J, Gao C, Jia X (2012) Using Hurst and Lyapunov exponent for hyperspectral image feature extraction. IEEE Geosci Remote Sens Lett 9:705–709CrossRefGoogle Scholar
  31. Yuan C, Sun X, Lv R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13:60–65CrossRefGoogle Scholar
  32. Zabalza J, Jinchang R, Zheng W, Marshall S, Jun W (2014a) Singular spectrum analysis for effective feature extraction in hyperspectral imaging. IEEE Geosci Remote Sens Lett 11:1886–1890CrossRefGoogle Scholar
  33. Zabalza J, Ren J, Yang M, Zhang Y, Wang J, Marshall S et al (2014b) Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS J Photogramm Remote Sens 93:112–122CrossRefGoogle Scholar
  34. Zhang H, Zhang L, Shen H (2012) A super-resolution reconstruction algorithm for hyperspectral images. Signal Process 92:2082–2096CrossRefGoogle Scholar
  35. Zhang E, Jiao L, Zhang X, Liu H, Wang S (2016) Class-level joint sparse representation for multifeature-based hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 9(9):4160–4177CrossRefGoogle Scholar
  36. Zheng Y, Jeon B, Xu D, Wu Q, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28:961–973Google Scholar
  37. Zhili Z, Ching-Nung Y, Xingming S, Qi L, WU QJ (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inf Syst 99:1531–1540Google Scholar
  38. Zhong Z, Fan B, Duan J, Wang L, Ding K, Xiang S et al (2015) Discriminant tensor spectral-spatial feature extraction for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12:1028–1032CrossRefGoogle Scholar
  39. Zhou Z, Wang Y, Wu QJ, Yang C-N, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12:48–63CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Gokulakrishnan Gopalan
    • 1
  • Tholkappia Arasu Govindarajan
    • 2
  1. 1.Department of Computer Science and EngineeringJayam College of Engineering and TechnologyDharmapuriIndia
  2. 2.Department of Electronics and Communication EngineeringAVS College of TechnologySalemIndia

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