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Parallel supervised land-cover classification system for hyperspectral and multispectral images

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Abstract

Hyperspectral and multispectral imagery allows remote-sensing applications such as the land-cover mapping, which is a significant baseline to understand and to monitor the Earth. Furthermore, it is a relevant process for socio-economic activities. For that reason, high land-classification accuracies are imperative, and minor image processing time is essential. In addition, the process of gathering classes’ documented samples is complicated. This implies that the classification system is required to perform with a limited number of training observations. Another point worth mentioning is that there are hardly any methods that can be used analogously for hyperspectral or multispectral images. This paper aims to propose a novel classification system that can be used for both types of images. The designed classification system is composed of a novel parallel feature extraction algorithm, which utilises a cluster of two graphics processing units in combination with a multicore central processing unit (CPU), and an artificial neural network (ANN) particularly devised for the classification of the features ensued by the implemented feature extraction method. To prove the performance of the proposed classification system, it is compared with non-parallel and CPU-only-parallel implementations employing multispectral and hyperspectral databases. Moreover, experiments with different number of samples for training the classifier are performed. Finally, the proposed ANN is compared with a state-of-the-art support vector machine in classification and processing time results.

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References

  1. Bay, H., Tuytelaars, T., Gool, L.V.: Surf: speeded up robust features. In: Computer Vision—ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol. 3951, pp. 404–417 (2006)

    Chapter  Google Scholar 

  2. Bianconi, F., Fernández, A.: Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognit. 40(12), 3325–3335 (2007)

    Article  Google Scholar 

  3. Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010)

    Article  Google Scholar 

  4. Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)

    Article  Google Scholar 

  5. Cao, X., Ji, Y., Wang, L., et al.: Fast hyperspectral band selection based on spatial feature extraction. J. Real-Time Image Proc. https://doi.org/10.1007/s11554-018-0777-9

    Article  Google Scholar 

  6. Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intelli. Syst. Technol. 2(3), 1–27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  7. Commission Internationale de l'Eclairage (CIE): ISO 11664-1:2007 (CIE S 014-1/E:206),​ Colorimetry—Part 1: CIE Standard Colorimetric Observers (2007)

  8. Clausi, D.A.: Comparison and fusion of cooccurrence, gabor and mrf texture features for classification of sar seaice imagery. Atmos. Ocean 39, 183–194 (2001)

    Article  Google Scholar 

  9. Colomina, I., Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 92, 79–97 (2014)

    Article  Google Scholar 

  10. Du, Q., Nekovei, R.: Fast real-time onboard processing of hyperspectral imagery for detection and classification. J. Real Time Image Process. 4(3), 273–286 (2009)

    Article  Google Scholar 

  11. Fleiss, J.L., Cohen, J.: The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ. Psychol. Meas. 33(3), 613–619 (1973)

    Article  Google Scholar 

  12. Garcia-Salgado, B.P., Ponomaryov, V.: Feature extraction scheme for a textural hyperspectral image classification using gray-scaled hsv and ndvi image features vectors fusion. CONIELECOMP 2016, 187–191 (2016)

    Google Scholar 

  13. Garcia-Salgado, B.P., Ponomaryov, V.I., Robles-Gonzalez, M.A.: Parallel multilayer perceptron neural network used for hyperspectral image classification. Proc. SPIE Real Time Image Video Process. 9897, 1–13 (2016)

    Google Scholar 

  14. GIC, U.o.t.B.C.: Hyperspectral remote sensing scenes. [Online] (2014). www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes. Accessed 13 June 2018

  15. Han, L., Li, L., Li, W.: GPU implementation of RX detection using spectral derivative features. J. Real-Time Image Proc. https://doi.org/10.1007/s11554-018-0773-0

    Article  Google Scholar 

  16. Haralick, R.M., Shanmugam, K.S., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3(6), 610–621 (1973)

    Article  Google Scholar 

  17. Hunt, R.W.G., Pointer, M.R.: Measuring colour. In: The Wiley-IS&T series in imaging science and technology, 4th edn, chap. 2, pp. 33–38. John Wiley & Sons, Hoboken, New Jersey, United States (2011)

    Google Scholar 

  18. Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. In: 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, pp. 14–19 (1990)

  19. Jain, M., Sinha, A.: Classification of satellite images through Gabor filter using SVM. Int. J. Comput. Appl. 116(7), 18–21 (2015)

    Google Scholar 

  20. Jia, S., Deng, B., et al.: Local binary pattern-based hyperspectral image classification with superpixel guidance. IEEE Trans. Geosci. Remote Sens. 56(2), 749–759 (2018)

    Article  MathSciNet  Google Scholar 

  21. Khatami, R., Mountrakis, G., Stehman, S.V.: A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research. Remote Sens. Environ. 177, 89–100 (2016)

    Article  Google Scholar 

  22. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 1, 159–174 (1977)

    Article  Google Scholar 

  23. Ma, L., Li, M., Ma, X., Cheng, L., Du, P., Liu, Y.: A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote Sens. 130, 277–293 (2017)

    Article  Google Scholar 

  24. Matikainen, L., Karila, K., Hyyppa, J., Litkey, P., Puttonen, E., Ahokas, E.: Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating. ISPRS J. Photogramm. Remote Sens. 128, 298–313 (2017)

    Article  Google Scholar 

  25. Monga, V., Mihçak, M.K.: Robust image hashing via non-negative matrix factorizations. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, pp. II-225–II-228 (2006)

  26. NVIDIA: Cuda llvm compiler. Online. https://developer.nvidia.com/cuda-llvm-compiler (2017). Accessed 20 Sept 2017

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

    Article  Google Scholar 

  28. Pan, L., Li, H.C., Ni, J., et al.: GPU-based fast hyperspectral image classification using joint sparse representation with spectral consistency constraint. J. Real-Time Image Proc. (2018). https://doi.org/10.1007/s11554-018-0775-y

    Article  Google Scholar 

  29. Panda, R.: Image segmentation by pixel classification in (gray level, edge value) space. IEEE Trans. Comput. C–27, 875–879 (1978)

    Article  Google Scholar 

  30. Paz, A., Plaza, A., Plaza, J.: Comparative analysis of different implementations of a parallel algorithm for automatic target detection and classification of hyperspectral images. Proc. SPIE Satell. Data Compress. Commun. Process. V 7455, 1–11 (2009)

    Google Scholar 

  31. Pinheiro, P.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1713–1721 (2015)

  32. Plaza, A., Plaza, J.: Parallel morphological classification of hyperspectral imagery using extended opening and closing by reconstruction operations. In: IEEE International Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008, pp. 58–61 (2008)

  33. Plaza, A.J.: Special issue on architectures and techniques for real-time processing of remotely sensed images. J. Real Time Image Process. 4(3), 191–193 (2009)

    Article  Google Scholar 

  34. Project, T.L.: The LLVM compiler infrastructure. [Online]. http://www.llvm.org (2017). Accessed 20 Sept 2017

  35. Puig, D., Garcia, M.A.: Automatic texture feature selection for image pixel classification. Pattern Recognit. 39(11), 1996–2009 (2006)

    Article  Google Scholar 

  36. Qin, C., Chen, X., et al.: A novel image hashing scheme with perceptual robustness using block truncation coding. Inf. Sci. 361–362, 84–89 (2016)

    Article  Google Scholar 

  37. Qin, C., Chen, X., et al.: Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Inf. Sci. 423, 284–302 (2018)

    Article  MathSciNet  Google Scholar 

  38. Quian, Y., Ye, M., Zhou, J.: Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans. Geosci. Remote Sens. 51(4), 2276–2291 (2012)

    Article  Google Scholar 

  39. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 International Conference on Computer Vision, pp. 2564–2571 (2011)

  40. Rud, R., Shoshany, M., Alchanatis, V., et al.: Application of spectral features’ ratios for improving classification in partially calibrated hyperspectral imagery: a case study of separating mediterranean vegetation species. J. Real Time Image Process. 1(2), 143–152 (2006)

    Article  Google Scholar 

  41. Salahat, E., Qasaimeh, M.: Recent advances in features extraction and description algorithms: a comprehensive survey. In: 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 1059 –1063 (2017)

  42. Saleem, S., Bais, A.: Feature points for multisensor images. Comput. Electr. Eng. 62, 511–523 (2017)

    Article  Google Scholar 

  43. Sandoval, G., Vazquez, R.A., Garcia, P., Ambrosio, J.: Crop classification using different color spaces and RBF neural networks. In: Lecture Notes in Computer Science, vol. 8467, pp. 598–609. Springer International Publishing (2014)

  44. Schistad Solberg, A.H., Jain, A.K.: Texture fusion and feature selection applied to sar imagery. IEEE Trans. Geosci. Remote Sens. 35(2), 475–479 (1997)

    Article  Google Scholar 

  45. Shao, Y., Lunetta, R.S.: Comparison of support vector machine, neural network, and cart algorithms for the land-cover classification using limited training data points. ISPRS J. Photogramm. Remote Sens. 70, 78–87 (2012)

    Article  Google Scholar 

  46. Shevell, S.K. (ed.): The Science of Color, Chap. 3, 2nd edn, pp. 110–115. Optical Society of America, Washington (2003)

    Google Scholar 

  47. Sima, A.A., Buckley, S.J.: Optimizing sift for matching of short wave infrared and visible wavelength images. Remote Sens. 5(5), 2037–2056 (2013)

    Article  Google Scholar 

  48. Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A.: SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 7(4), 736–740 (2010)

    Article  Google Scholar 

  49. Tarabalka, Y., Haavardsholm, T.V., Kasen, I., et al.: Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing. J. Real Time Image Process. 4(3), 287–300 (2009)

    Article  Google Scholar 

  50. Tsai, F., Lai, J.S.: Feature extraction of hyperspectral image cubes using three-dimensional gray-level cooccurrence. IEEE Trans. Geosci. Remote Sens. 51(6), 3504–3513 (2013)

    Article  Google Scholar 

  51. Volpi, M., Ferrari, V.: Semantic segmentation of urban scenes by learning local class interactions. In: IEEE CVPR 2015 Workshop Looking from Above: When Earth Observation Meets Vision. IEEE Press (2015)

  52. Wu, Z., Wang, Q., Plaza, A., Li, J., Sun, L., Wei, Z.: Parallel spatial-spectral hyperspectral image classification with sparse representation and markov random fields on gpus. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 8(6), 2926–2938 (2015)

    Article  Google Scholar 

  53. Xian, G., Homer, C., Fry, J.: Updating the 2001 national land cover database land cover classification to 2006 by using landsat imagery change detection methods. Remote Sens. Environ. 113(6), 1133–1147 (2009)

    Article  Google Scholar 

  54. Ye, Z., Fowler, J.E., Bai, L.: Spatial-spectral hyperspectral classification using local binary patterns and Markov random fields. J. Appl. Remote Sens. 11(3), 1–14 (2017)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Consejo Nacional de Ciencias y Tecnologia (CONACyT, Grant 220347) and Instituto Politecnico Nacional (IPN) for their support. In addition, they would like to thank the Computational Intelligence Group from the Basque University and Michele Volpi from University of Zurich for making, online available, the hyperspectral and multispectral databases correspondingly.

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Authors and Affiliations

  1. Instituto Politecnico Nacional, Mexico City, Mexico

    Beatriz P. Garcia-Salgado, Volodymyr I. Ponomaryov & Marco Robles-Gonzalez

  2. Instituto Mexicano del Petroleo, Mexico City, Mexico

    Sergiy Sadovnychiy

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  1. Beatriz P. Garcia-Salgado
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  2. Volodymyr I. Ponomaryov
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  3. Sergiy Sadovnychiy
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  4. Marco Robles-Gonzalez
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Correspondence to Beatriz P. Garcia-Salgado.

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Garcia-Salgado, B.P., Ponomaryov, V.I., Sadovnychiy, S. et al. Parallel supervised land-cover classification system for hyperspectral and multispectral images. J Real-Time Image Proc 15, 687–704 (2018). https://doi.org/10.1007/s11554-018-0828-2

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