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Multiple features learning for ship classification in optical imagery

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Abstract

The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods.

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Notes

  1. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

References

  1. Arguedas VF (2015) Texture-based vessel classifier for electro-optical satellite imagery. In: IEEE international conference on image processing (ICIP), 2015, pp 3866–3870

  2. Barnum J (1986) Ship detection with high-resolution hf skywave radar. IEEE J Ocean Eng 11(2):196–209

    Article  Google Scholar 

  3. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. Bosch A, Zisserman A, Munoz X (2007) Image classification using random forests and ferns. In: 2007 IEEE 11th international conference on computer vision, pp 1550–5499

  6. Chang C, Lin C (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):389–396

    Article  Google Scholar 

  7. Chen C, Zhang B, Su H, Li W, Wang L (2015) Land-use scene classification using multi-scale completed local binary patterns. Signal Image and Video Processing 1–8

  8. Clausi DA, Jernigan ME (2000) Designing gabor filters for optimal texture separability. Pattern Recogn 33(11):1835–1849

    Article  Google Scholar 

  9. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1, pp 886–893

  10. Guo W, Xia X, Wang X (2014) A remote sensing ship recognition method based on dynamix probablity generative model. Expert Syst Appl 41(14):6446–6458

    Article  Google Scholar 

  11. Guo W, Xia X, Wang X (2015) A remote sensing ship recognition method for entropy-based hierarchical discriminant regression. Optik-International Journal for Light and Electron Optics 126(20):2300–2307

    Article  Google Scholar 

  12. Guo W, Xia X, Wang X (2015) Variational approximate inferential probability generative model for ship recognition using remote sensing data. Optik-International Journal for Light and Electron Optics 126(23):4004–4013

    Article  Google Scholar 

  13. Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  MATH  Google Scholar 

  14. Guo Z, Zhang L, Zhang D, Mou X (2010) Hierarchical multiscale lbp for face and palmprint recognition. In: 2010 IEEE international conference on image processing, Hong Kong, pp 4521–4524

  15. Harguess J, Rainey K (2011) Are face recognition methods useful for classifying ships?. In: Applied imagery pattern recognition workshop, pp 1–7

  16. Huang L, Chen C, Li W, Du Q (2016) Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors. Remote Sens 8(6)

  17. Huang S, Xu H, Xia X (2016) Active deep belief networks for ship recognition based on BvSB. Optik-International Journal for Light and Electron Optics 24:11688–11697

    Article  Google Scholar 

  18. Jaakkola TS, Haussler D (1998) Exploiting generative models in discriminative classifiers. Adv Neural Inf Proces Syst 11(11):487–493

    Google Scholar 

  19. Jun Y, YuGang J, Hauptmann AG, Chong Wah N (2007) Evaluating bag-of-visual-words representations in scene classification. In: ACM Sigmm international workshop on multimedia information retrieval, pp 197–206

  20. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2, pp 2169–2178

  21. Li W, Chen C, Su H, Du Q (2015) Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens 53(7):3681–3693

    Article  Google Scholar 

  22. Li W, Prasad S, Fowler JE (2014) Decision fusion in kernel-induced spaces for hyperspectral image classification. IEEE Trans Geosci Remote Sens 52(6):3399–3411

    Article  Google Scholar 

  23. Liu C (2003) Maximum likelihood estimation from incomplete data via em-type algorithms. Advanced Medical Statistics 1051–1071

  24. Lowe DG (2004) Distinctive image features from scale-invariant key points. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Lu H, Plataniotis KN, Anastasios N (2008) Mpca: Multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39

    Article  Google Scholar 

  26. Margarit G, Milanes J, Tabasco A (2009) Operational ship monitoring system based on synthetic aperture Radar processing. Remote Sens 1(3):375–392

    Article  Google Scholar 

  27. Ojala T, Pietikainen M, Maenpaa TT (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  28. Parameswaran S, Rainey K (2015) Vessel classification in overhead satellite imagery using weighted bag of visual words. In: Automatic target recognition XXV

  29. Park S, Cho CJ, Ku B, Lee S, Ko H (2016) Simulation and ship detection using surface radial current observing compact HF radar. IEEE J Ocean Eng 99:1–12

    Google Scholar 

  30. Peng B, Li W, Xie X, Du Q, Liu K (2015) Weighted-fusion-based representation classifiers for hyperspectral imagery. Remote Sens 7

  31. Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8

  32. Prasad S, Bruce LM (2008) Decision fusion with confidence-based weight assignment for hyperspectral target recognition. IEEE Trans Geosci Remote Sens 46(5):1448–1456

    Article  Google Scholar 

  33. Prasad S, Li W, Fowler JE, Bruce LM (2012) Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification. IEEE Trans Geosci Remote Sens 50(9):3474–3486

    Article  Google Scholar 

  34. Rainey K, Parameswaran S, Harguess J (2014) Maritime vessel recognition in degraded satellite imagery. Proc SPIE-Int Soc Opt Eng 9090(9):909004–909004–6

    Google Scholar 

  35. Rainey K, Parameswaran S, Harguess J, Stastny J (2012) Vessel classification in overhead satellite imagery using learned dictionaries. In: Proceedings of SPIE-the international society for optical engineering, vol 8499, pp 84992–12

  36. Rainey K, Stastny J (2011) Object recognition in ocean imagery using feature selection and compressive sensing. In: 2011 IEEE applied imagery pattern recognition workshop, pp 1–6

  37. Sanchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    Article  MathSciNet  MATH  Google Scholar 

  38. Teng F, Liu Q (2015) Robust multi-scale ship tracking via multiple compressed features fusion. Signal Process Image Commun 31:76–85

    Article  Google Scholar 

  39. Toh KA, Kim J, Lee S (2008) Biometric scores fusion based on total error rate minimization. Pattern Recogn 41(3):1066–1082

    Article  MATH  Google Scholar 

  40. Verbancsics P, Harguess J (2015) Feature learning hyperneat: evolving neural networks to extract features for classification of maritime satellite imagery. Springer International Publishing, pp 208–220

  41. Verbancsics P, Harguess J (2015) Image classification using generative neuro evolution for deep learning. In: 2015 IEEE winter conference on applications of computer vision, pp 1550–5790

  42. Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  43. Xin H, Zhang L (2013) An svm ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens 51(1):257–272

    Article  MathSciNet  Google Scholar 

  44. Xu Y, Lu Y (2015) Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168:566–574

    Article  Google Scholar 

  45. Xu Y, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recogn Lett 68:9–14

    Article  Google Scholar 

  46. Xu Y, Zhu X, Li Z, Liu G, Lu Y, Liu H (2013) Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn 46(4):1151–1158

    Article  Google Scholar 

  47. Yang G, Liu H, Yu X (2007) Hyperspectral remote sensing image classification based on kernel fisher discriminant analysis. In: 2007 international conference on wavelet analysis and pattern recognition, vol 3, pp 1139–1143

  48. Zhang MM, Choi J, Daniilidis K, Wolf MT, Kanan C (2015) Vais: a dataset for recognizing maritime imagery in the visible and infrared spectrums. In: 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 10–16

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China

    Longhui Huang, Wei Li & Fan Zhang

  2. Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA

    Chen Chen

  3. Faculty of Science, Beijing University of Chemical Technology, Beijing, 100029, China

    Haitao Lang

Authors
  1. Longhui Huang
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  2. Wei Li
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  3. Chen Chen
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  4. Fan Zhang
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  5. Haitao Lang
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Corresponding author

Correspondence to Wei Li.

Additional information

This work was supported by the National Key Research and Development Program of China under Grant 2016YFB0501501, and partly by the Higher Education and High-Quality and World-Class Universities under Grant PY201619.

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Huang, L., Li, W., Chen, C. et al. Multiple features learning for ship classification in optical imagery. Multimed Tools Appl 77, 13363–13389 (2018). https://doi.org/10.1007/s11042-017-4952-y

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  • DOI: https://doi.org/10.1007/s11042-017-4952-y

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