Skip to main content
SpringerLink
Log in

Satellite Image Clustering

  • Chapter
  • First Online:
Satellite Image Analysis: Clustering and Classification

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSINTELL))

  • 1220 Accesses

Abstract

Remote Sensing technology senses and measures the radiation or reflectance of samples of distant objects, and allows extraction of information which includes detection and recognition of objects and its coverage. Image classification methods identify the objects represented by each pixel in the satellite image based on its spectral wavelength and time series. In this chapter, the basics of satellite image classification and its types are presented. The unsupervised classification methods such as K-means, Gaussian mixture model, self-organizing maps, and Hidden Markov models are described for clustering of satellite images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dey, N., Bhatt, C., & Ashour, A. S. (2018). Big data for remote sensing: Visualization, analysis and interpretation. Cham: Springer.

    Google Scholar 

  2. Li, Z., Dey, N., Ashour, A. S., Cao, L., Wang, Y., Wang, D., … Shi, F. (2017). Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset. Journal of Medical Imaging and Health Informatics, 7(3), 639–652.

    Article  Google Scholar 

  3. Chakrabarty, S., Pal, A. K., Dey, N., Das, D., & Acharjee, S. (2014, January). Foliage area computation using Monarch butterfly algorithm. In 2014 1st International Conference on Non-conventional Energy (ICONCE) (pp. 249–253). IEEE.

    Google Scholar 

  4. Wagstaff, K., Cardie, C., Rogers, S., & Schrödl, S. (2001, June). Constrained k-means clustering with background knowledge. In ICML (Vol. 1, pp. 577–584).

    Google Scholar 

  5. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society Series C (Applied Statistics), 28(1), 100–108.

    Article  MATH  Google Scholar 

  6. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7, 881–892.

    Article  MATH  Google Scholar 

  7. Kale, S., & Bere, S. (2015). An efficient k-means clustering algorithm. International Journal of  Engineering, Education and Technology, 3(2), 1–8.

    Google Scholar 

  8. Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461.

    Article  Google Scholar 

  9. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis (Vol. 344). USA: Wiley.

    Google Scholar 

  10. Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.

    Google Scholar 

  11. Mehrotra, K., Mohan, C. K., & Ranka, S. (1997). Elements of artificial neural networks. Cambridge: MIT Press.

    Google Scholar 

  12. Bandyopadhyay, S., & Maulik, U. (2002). An evolutionary technique based on K-means algorithm for optimal clustering in RN. Information Sciences, 146(1–4), 221–237.

    Article  MathSciNet  MATH  Google Scholar 

  13. Bose, S., Mukherjee, A., Chakraborty, S., Samanta, S., & Dey, N. (2013, December). Parallel image segmentation using multi-threading and k-means algorithm. In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1–5). IEEE.

    Google Scholar 

  14. Han, K. S., Champeaux, J. L., & Roujean, J. L. (2004). A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data. Remote Sensing of Environment, 92(1), 52–66.

    Article  Google Scholar 

  15. Mitra, P., Shankar, B. U., & Pal, S. K. (2004). Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognition Letters, 25(9), 1067–1074.

    Article  Google Scholar 

  16. Rekik, A., Zribi, M., Hamida, A. B., & Benjelloun, M. (2009). An optimal unsupervised satellite image segmentation approach based on Pearson system and k-means clustering algorithm initialization. Methods, 8, 9.

    Google Scholar 

  17. Sathya, P., & Malathi, L. (2011). Classification and segmentation in satellite imagery using back propagation algorithm of ANN and k-means algorithm. International Journal of Machine Learning and Computing, 1(4), 422.

    Article  Google Scholar 

  18. Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928–4943.

    Article  Google Scholar 

  19. Hu, F., Xia, G. S., Wang, Z., Huang, X., Zhang, L., & Sun, H. (2015). Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2015–2030.

    Article  Google Scholar 

  20. Li, Y., Tao, C., Tan, Y., Shang, K., & Tian, J. (2016). Unsupervised multilayer feature learning for satellite image scene classification. IEEE Geoscience and Remote Sensing Letters, 13(2), 157–161.

    Article  Google Scholar 

  21. Biernacki, C., Celeux, G., & Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719–725.

    Article  Google Scholar 

  22. Biernacki, C., Celeux, G., & Govaert, G. (2003). Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics & Data Analysis, 41(3–4), 561–575.

    Article  MathSciNet  MATH  Google Scholar 

  23. Zivkovic, Z. (2004, August). Improved adaptive Gaussian mixture model for background subtraction. In ICPR 2004. Proceedings of the 17th International Conference on Pattern Recognition (Vol. 2, pp. 28–31). IEEE.

    Google Scholar 

  24. Maugis, C., Celeux, G., & Martin-Magniette, M. L. (2009). Variable selection for clustering with Gaussian mixture models. Biometrics, 65(3), 701–709.

    Article  MathSciNet  MATH  Google Scholar 

  25. McLachlan, G., & Peel, D. (2000). Finite mixture models. Wiley series in probability and statistics.

    Google Scholar 

  26. Wang, D., Li, Z., Cao, L., Balas, V. E., Dey, N., Ashour, A. S., … Shi, F. (2017). Image fusion incorporating parameter estimation optimized Gaussian mixture model and fuzzy weighted evaluation system: A case study in time-series plantar pressure data set. IEEE Sensors Journal, 17(5), 1407–1420. USA: Wiley.

    Google Scholar 

  27. Stauffer, C., & Grimson, W. E. L. (2000). Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 747–757.

    Article  Google Scholar 

  28. Ju, J., Kolaczyk, E. D., & Gopal, S. (2003). Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing. Remote Sensing of Environment, 84(4), 550–560.

    Article  Google Scholar 

  29. Liu, W., & Wu, E. Y. (2005). Comparison of non-linear mixture models: Sub-pixel classification. Remote Sensing of Environment, 94(2), 145–154.

    Article  MathSciNet  Google Scholar 

  30. Bazi, Y., Bruzzone, L., & Melgani, F. (2005). An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 874–887.

    Article  Google Scholar 

  31. Doulgeris, A. P., Anfinsen, S. N., & Eltoft, T. (2008). Classification with a non-Gaussian model for PolSAR data. IEEE Transactions on Geoscience and Remote Sensing, 46(10), 2999–3009.

    Article  Google Scholar 

  32. Kerroum, M. A., Hammouch, A., & Aboutajdine, D. (2010). Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification. Pattern Recognition Letters, 31(10), 1168–1174.

    Article  Google Scholar 

  33. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.

    Article  MathSciNet  MATH  Google Scholar 

  34. Kohonen, T. (1982). Analysis of a simple self-organizing process. Biological Cybernetics, 44(2), 135–140.

    Article  MathSciNet  MATH  Google Scholar 

  35. Ritter, H., & Kohonen, T. (1989). Self-organizing semantic maps. Biological Cybernetics, 61(4), 241–254.

    Article  Google Scholar 

  36. Kangas, J. A., Kohonen, T. K., & Laaksonen, J. T. (1990). Variants of self-organizing maps. IEEE Transactions on Neural Networks, 1(1), 93–99.

    Article  Google Scholar 

  37. Erwin, E., Obermayer, K., & Schulten, K. (1992). Self-organizing maps: Ordering, convergence properties and energy functions. Biological Cybernetics, 67(1), 47–55.

    Article  MATH  Google Scholar 

  38. Kaski, S., Honkela, T., Lagus, K., & Kohonen, T. (1998). WEBSOM—Self-organizing maps of document collections1. Neurocomputing, 21(1–3), 101–117.

    Article  MATH  Google Scholar 

  39. Dittenbach, M., Merkl, D., & Rauber, A. (2000). The growing hierarchical self-organizing map. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000 (Vol. 6, pp. 15–19). IEEE.

    Google Scholar 

  40. Kamal, M. S., Sarowar, M. G., Dey, N., Ashour, A. S., Ripon, S. H., Panigrahi, B. K., & Tavares, J. M. R. (2017). Self-organizing mapping-based swarm intelligence for secondary and tertiary proteins classification. International Journal of Machine Learning and Cybernetics, 1–24.

    Google Scholar 

  41. Arias, S., Gómez, H., Prieto, F., Botón, M., & Ramos, R. (2009). Satellite image classification by self-organized maps on GRID computing infrastructures. In Proceedings of the second EELA-2 Conference (pp. 1–11).

    Google Scholar 

  42. Awad, M. (2010). Segmentation of satellite images using self-organizing maps. In Self-organizing maps. InTech.

    Google Scholar 

  43. Santos, M. D., Shiguemori, E. H., Mota, R. L., & Ramos, A. C. (2015, April). Change detection in satellite images using self-organizing maps. In 2015 12th International Conference on Information Technology-New Generations (ITNG) (pp. 662–667). IEEE.

    Google Scholar 

  44. Ji, C. Y. (2000). Land-use classification of remotely sensed data using Kohonen self-organizing feature map neural networks. Photogrammetric Engineering and Remote Sensing, 66(12), 1451–1460.

    Google Scholar 

  45. Richardson, A. J., Risien, C., & Shillington, F. A. (2003). Using self-organizing maps to identify patterns in satellite imagery. Progress in Oceanography, 59(2–3), 223–239.

    Article  Google Scholar 

  46. Jianwen, M., & Bagan, H. (2005). Land-use classification using ASTER data and self-organized neutral networks. International Journal of Applied Earth Observation and Geoinformation, 7(3), 183–188.

    Article  Google Scholar 

  47. Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10), 2089–2102.

    Article  Google Scholar 

  48. Nourani, V., Baghanam, A. H., Adamowski, J., & Gebremichael, M. (2013). Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network-based rainfall–runoff modeling. Journal of Hydrology, 476, 228–243.

    Article  Google Scholar 

  49. Neagoe, V. E., Stoica, R. M., Ciurea, A. I., Bruzzone, L., & Bovolo, F. (2014). Concurrent self-organizing maps for supervised/unsupervised change detection in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8), 3525–3533.

    Article  Google Scholar 

  50. Ghosh, S., Roy, M., & Ghosh, A. (2014). Semi-supervised change detection using modified self-organizing feature map neural network. Applied Soft Computing, 15, 1–20.

    Article  Google Scholar 

  51. Kussul, N., Lemoine, G., Gallego, J., Skakun, S., & Lavreniuk, M. (2015, July). Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 165–168). IEEE.

    Google Scholar 

  52. Kamal, M. S., Chowdhury, L., Khan, M. I., Ashour, A. S., Tavares, J. M. R., & Dey, N. (2017). Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images. Computational Biology and Chemistry, 68, 231–244.

    Article  Google Scholar 

  53. Wang, Q. (2012). HMRF-EM-image: Implementation of the hidden Markov random field model and its expectation-maximization algorithm. arXiv:1207.3510.

  54. Li, J., Najmi, A., & Gray, R. M. (2000). Image classification by a two-dimensional hidden Markov model. IEEE Transactions on Signal Processing, 48(2), 517–533.

    Article  Google Scholar 

  55. Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45–57.

    Article  Google Scholar 

  56. Fjortoft, R., Delignon, Y., Pieczynski, W., Sigelle, M., & Tupin, F. (2003). Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields. IEEE Transactions on Geoscience and Remote Sensing, 41(3), 675–686.

    Article  Google Scholar 

  57. Xu, K., Yang, W., Liu, G., & Sun, H. (2013). Unsupervised satellite image classification using Markov field topic model. IEEE Geoscience and Remote Sensing Letters, 10(1), 130–134.

    Article  Google Scholar 

  58. Voisin, A., Krylov, V. A., Moser, G., Serpico, S. B., & Zerubia, J. (2013). Classification of very high-resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model. IEEE Geoscience and Remote Sensing Letters, 10(1), 96–100.

    Article  Google Scholar 

  59. Subudhi, B. N., Bovolo, F., Ghosh, A., & Bruzzone, L. (2014). Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images. Optics & Laser Technology, 57, 284–292.

    Article  Google Scholar 

  60. Siachalou, S., Doxani, G., & Tsakiri-Strati, M. (2014, May). Time-series analysis of high temporal remote sensing data to model wetland dynamics: A hidden Markov model approach. In Proceedings of the SENTINEL-2 for Science Workshop—ESA-ESRIN, Frascati, Italy (pp. 20–22).

    Google Scholar 

  61. Yuan, Y., Meng, Y., Lin, L., Sahli, H., Yue, A., Chen, J., … He, D. (2015). Continuous change detection and classification using hidden Markov model: A case study for monitoring urban encroachment onto farmland in Beijing. Remote Sensing, 7(11), 15318–15339.

    Article  Google Scholar 

  62. Siachalou, S., Mallinis, G., & Tsakiri-Strati, M. (2015). A hidden Markov models approach for crop classification: Linking crop phenology to time series of multi-sensor remote sensing data. Remote Sensing, 7(4), 3633–3650.

    Article  Google Scholar 

  63. Ripon, S. H., Kamal, S., Hossain, S., & Dey, N. (2016). Theoretical analysis of different classifiers under reduction rough data set: A brief proposal. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(3), 1–20.

    Article  Google Scholar 

  64. Dev, S., Wen, B., Lee, Y. H., & Winkler, S. (2016). Ground-based image analysis: A tutorial on machine-learning techniques and applications. IEEE Geoscience and Remote Sensing Magazine, 4(2), 79–93.

    Article  Google Scholar 

  65. Dev, S., Wen, B., Lee, Y. H., & Winkler, S. (2016). Machine learning techniques and applications for ground-based image analysis. arXiv:1606.02811.

  66. Lowe, D. G. (1999). Object recognition from local scale-invariant features. In The Proceedings of the Seventh IEEE International Conference on Computer Vision (Vol. 2, pp. 1150–1157). IEEE.

    Google Scholar 

  67. Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  68. Harris, C., & Stephens, M. (1988, August). A combined corner and edge detector. In Alvey Vision Conference (Vol. 15, No. 50, pp. 10–5244).

    Google Scholar 

  69. Sedaghat, A., Mokhtarzade, M., & Ebadi, H. (2011). Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4516–4527.

    Article  Google Scholar 

  70. Li, Q., Wang, G., Liu, J., & Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287–291.

    Article  Google Scholar 

  71. Xu, X., & Miller, E. L. (2002, June). Adaptive difference of Gaussians to improve subsurface imagery. In 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2002 (Vol. 6, pp. 3441–3443). IEEE.

    Google Scholar 

  72. Upla, K. P., Joshi, M. V., & Gajjar, P. P. (2014, July). Pan-sharpening: Use of difference of Gaussians. In 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 4922–4925). IEEE.

    Google Scholar 

  73. Tokarczyk, P., Wegner, J. D., Walk, S., & Schindler, K. (2013). Beyond hand-crafted features in remote sensing. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 35–40.

    Article  Google Scholar 

  74. Arenas-Garcia, J., Petersen, K. B., Camps-Valls, G., & Hansen, L. K. (2013). Kernel multivariate analysis framework for supervised subspace learning: A tutorial on linear and kernel multivariate methods. IEEE Signal Processing Magazine, 30(4), 16–29.

    Article  Google Scholar 

  75. Aharon, M., Elad, M., & Bruckstein, A. (2006). k-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.

    Article  MATH  Google Scholar 

  76. Jolliffe, I. (2011). Principal component analysis. In International encyclopedia of statistical science (pp. 1094–1096). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  77. Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433–459.

    Article  Google Scholar 

  78. Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776.

    Article  Google Scholar 

  79. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52.

    Article  Google Scholar 

  80. Kwarteng, P., & Chavez, A. (1989). Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing, 55, 339–348.

    Google Scholar 

  81. Rodarmel, C., & Shan, J. (2002). Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), 115–122.

    Google Scholar 

  82. Schowengerdt, R. A. (2006). Remote sensing: Models and methods for image processing. Burlington, NJ: Elsevier.

    Google Scholar 

  83. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB (Vol. 624). Upper Saddle River, NJ: Pearson-Prentice-Hall.

    Google Scholar 

  84. Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2009). Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP Journal on Advances in Signal Processing, 2009(1), 783194.

    Article  Google Scholar 

  85. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.

    Article  MATH  Google Scholar 

  86. Xiao Benlin, A., Li Fangfang, B., Mao Xingliang, C., & Jin Huazhong, B. (2008). Study on independent component analysis’ application in classification and change detection of multispectral images. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XXXVII, Part B7, pp. 871–876). Beijing 2008.

    Google Scholar 

  87. 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 421–435.

    Article  Google Scholar 

  88. Al-Taei, M. S. M., & Al-Ghrairi, A. H. T. (2016). Satellite image classification using moment and SVD method. International Journal of Computer (IJC), 23(1), 10–34.

    Google Scholar 

  89. Brindha, S. (2015). Satellite image enhancement using DWT–SVD and segmentation using MRR–MRF model. Journal of Network Communications and Emerging Technologies (JNCET), 1(1), 6–10.

    Google Scholar 

  90. Ranjith, K. J., Thomas, H. A., & Stamp, M. (2014). Singular value decomposition and metamorphic detection. Journal of Computer Virology and Hacking Techniques, 11(4), 203–216.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, K.S. Institute of Technology, Bengaluru, Karnataka, India

    Surekha Borra

  2. Faculty of Technology and Engineering, Department of ECE, C. U. Shah University, Wadhwan city, Gujarat, India

    Rohit Thanki

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

Authors
  1. Surekha Borra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rohit Thanki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nilanjan Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Borra, S., Thanki, R., Dey, N. (2019). Satellite Image Clustering. In: Satellite Image Analysis: Clustering and Classification. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6424-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6424-2_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6423-5

  • Online ISBN: 978-981-13-6424-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation