Skip to main content
SpringerLink
Log in

Satellite Image Classification

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

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

Abstract

This chapter presents the traditional supervised classification methods and then focuses on the state of the art automated satellite image classification methods such as Nearest Neighbours, Naive Bayes, Support Vector Machine (SVM), Discriminant Analysis, Random Forests, Decision Trees, Semi-supervised, Convolutional neural network Models, Deep Convolutional Neural Networks and Hybrid Approaches.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
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. Jawak, S. D., Devliyal, P., & Luis, A. J. (2015). A comprehensive review on pixel oriented and object-oriented methods for information extraction from remotely sensed satellite images with a special emphasis on cryospheric applications. Advances in Remote Sensing, 4(3), 177.

    Article  Google Scholar 

  2. Dey, N., Bhatt, C., & Ashour, A. S. (2018). Big data for remote sensing: Visualization, Analysis and Interpretation. Cham: Springer.

    Google Scholar 

  3. Tan, K. C., Lim, H. S., & Jafri, M. M. (2011, September). Comparison of neural network and maximum likelihood classifiers for land cover classification using Landsat multispectral data. In 2011 IEEE Conference on Open Systems (ICOS) (pp. 241-244). IEEE.

    Google Scholar 

  4. Paneque-Gálvez, J., Mas, J. F., Moré, G., Cristóbal, J., Orta-Martínez, M., Luz, A. C., … & Reyes-García, V. (2013). Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. International Journal of Applied Earth Observation and Geoinformation, 23, 372–383

    Article  Google Scholar 

  5. Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X., & Li, B. (2014). Land cover classification using Landsat 8 operational land imager data in Beijing, China. Geocarto International, 29(8), 941–951.

    Article  Google Scholar 

  6. Liu, T., & Yang, X. (2015). Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography, 56, 42–54.

    Article  Google Scholar 

  7. Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84.

    Article  Google Scholar 

  8. Jog, S., & Dixit, M. (2016, June). Supervised classification of satellite images. In 2016 Conference on Advances in Signal Processing (CASP)  (pp. 93–98). IEEE.

    Google Scholar 

  9. Zhang, H. (2004). The optimality of naive Bayes. AA, 1(2), 3.

    Google Scholar 

  10. Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39). Cambridge University Press.

    Google Scholar 

  11. McCallum, A., & Nigam, K. (1998, July). A comparison of event models for naive bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization (Vol. 752, No. 1, pp. 41–48).

    Google Scholar 

  12. Metsis, V., Androutsopoulos, I., & Paliouras, G. (2006, July). Spam filtering with naive bayes-which naive bayes? In CEAS (Vol. 17, pp. 28–69).

    Google Scholar 

  13. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2), 179–188.

    Article  Google Scholar 

  14. Qian, Y., Zhou, W., Yan, J., Li, W., & Han, L. (2014). Comparing machine learning classifiers for object-based land cover classification using very high-resolution imagery. Remote Sensing, 7(1), 153–168.

    Article  Google Scholar 

  15. Alonso-Montesinos, J., Martínez-Durbán, M., del Sagrado, J., del Águila, I. M., & Batlles, F. J. (2016). The application of Bayesian network classifiers to cloud classification in satellite images. Renewable Energy, 97, 155–161.

    Article  Google Scholar 

  16. Sitthi, A., Nagai, M., Dailey, M., & Ninsawat, S. (2016). Exploring land use and land cover of geotagged social-sensing images using naive bayes classifier. Sustainability, 8(9), 921.

    Article  Google Scholar 

  17. Tsangaratos, P., & Ilia, I. (2016). Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. CATENA, 145, 164–179.

    Article  Google Scholar 

  18. Pham, B. T., Bui, D. T., Pourghasemi, H. R., Indra, P., & Dholakia, M. B. (2017). Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology, 128(1–2), 255–273.

    Article  Google Scholar 

  19. Xia, M., Lu, W., Yang, J., Ma, Y., Yao, W., & Zheng, Z. (2015). A hybrid method based on extreme learning machine and k-nearest neighbor for cloud classification of ground-based visible cloud image. Neurocomputing, 160, 238–249.

    Article  Google Scholar 

  20. Son, N. T., Chen, C. F., Chang, N. B., Chen, C. R., Chang, L. Y., & Thanh, B. X. (2015). Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 503–510.

    Article  Google Scholar 

  21. Nair, M., & Bindhu, J. S. (2016). Supervised techniques and approaches for satellite image classification. International Journal of Computer Applications, 134(16).

    Article  Google Scholar 

  22. González, M. P., Bonaccorso, E., & Papeş, M. (2015). Applications of geographic information systems and remote sensing techniques to conservation of amphibians in northwestern Ecuador. Global Ecology and Conservation, 3, 562–574.

    Article  Google Scholar 

  23. Serban, C., & Maftei, C. (2011). Thermal analysis of climate regions using remote sensing and grid computing. arXiv preprint arXiv:1101.4474.

  24. Yen, S. H., & Wang, C. J. (2006). SVM based watermarking technique. Tamkang Journal of Science and Engineering, 9(2), 141–150.

    MathSciNet  Google Scholar 

  25. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer.

    Book  MATH  Google Scholar 

  26. Hsu, C. W., Chang, C. C., & Lin, C. J. (2016). A practical guide to support vector classification. Retrieved February, 2018, from https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

  27. Liaw, A., & Wiener, M. (2002). Classification and Regression by Random Forest. R news, 2(3), 18–22.

    Google Scholar 

  28. Dalponte, M., Ørka, H. O., Ene, L. T., Gobakken, T., & Næsset, E. (2014). Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment, 140, 306–317.

    Article  Google Scholar 

  29. Schmedtmann, J., & Campagnolo, M. L. (2015). Reliable crop identification with satellite imagery in the context of common agriculture policy subsidy control. Remote Sensing, 7(7), 9325–9346.

    Article  Google Scholar 

  30. Zhang, F., Du, B., & Zhang, L. (2015). Saliency-guided unsupervised feature learning for scene classification. IEEE Transactions on Geoscience and Remote Sensing, 53(4), 2175–2184.

    Article  Google Scholar 

  31. Rao, C. R. (1948). The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 159–203.

    MathSciNet  MATH  Google Scholar 

  32. Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (1999, August). Fisher discriminant analysis with kernels. In  Proceedings of the 1999 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing (pp. 41–48). IEEE.

    Google Scholar 

  33. Friedman, J. H. (1989). Regularized discriminant analysis. Journal of the American statistical association, 84(405), 165–175.

    Article  MathSciNet  Google Scholar 

  34. Raschka, S. (2014). Linear discriminant analysis. Retrieved August, 2018, from https://sebastianraschka.com/Articles/2014_python_lda.html.

  35. Saxena, R. (2017). How decision tree algorithm works. Retrieved August, 2018, from https://dataaspirant.com/2017/01/30/how-decision-tree-algorithm-works/.

  36. Kulkarni, A. D., & Shrestha, A. (2017). Multispectral image analysis using decision trees. International Journal of Advanced Computer Science and Applications, 8(6), 11–18.

    Google Scholar 

  37. Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1), 81–106.

    Google Scholar 

  38. Ghose, M. K., Pradhan, R., & Ghose, S. S. (2010). Decision tree classification of remotely sensed satellite data using spectral separability matrix. International Journal of Advanced Computer Science and Applications, 1(5), 93–101.

    Google Scholar 

  39. Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R news, 2(3), 18–22.

    Google Scholar 

  40. Segal, M. R. (2004). Machine learning benchmarks and random forest regression. Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  41. Cootes, T. F., Ionita, M. C., Lindner, C., & Sauer, P. (2012, October). Robust and accurate shape model fitting using random forest regression voting. In European Conference on Computer Vision (pp. 278–291). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  42. Dassenakis, M., Paraskevopoulou, V., Cartalis, C., Adaktilou, N., & Katsiabani, K. (2011). Remote sensing in coastal water monitoring: Applications in the eastern Mediterranean Sea (IUPAC Technical Report). Pure and Applied Chemistry, 84(2), 335–375.

    Article  Google Scholar 

  43. Almendros-Jiménez, J. M., Domene, L., & Piedra-Fernández, J. A. (2013). A framework for ocean satellite image classification based on ontologies. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2), 1048–1063.

    Article  Google Scholar 

  44. Alganci, U., Sertel, E., & Kaya, S. (2018). Determination of the olive trees with object-based classification of Pleiades satellite image. International Journal of Environment and Geoinformatics 5, 132–139.

    Article  Google Scholar 

  45. Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., … & Balling, R. C. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275.

    Article  Google Scholar 

  46. Classification Tutorial. (2018). Retrieved October, 2018, from https://www.harrisgeospatial.com/docs/Classification.html.

  47. Huan, X. I. E., & Lohmann, P. (2008). A new binary encoding algorithm for the integration of hyperspectral data and DSM.

    Google Scholar 

  48. Girouard, G., Bannari, A., El Harti, A., & Desrochers, A. (2004, July). Validated spectral angle mapper algorithm for geological mapping: Comparative study between QuickBird and Landsat-TM. In XXth ISPRS Congress, Geo-imagery Bridging Continents, Istanbul, Turkey (pp. 12–23).

    Google Scholar 

  49. Shivakumar, B. R., & Rajashekararadhya, S. V. (2017, February). Performance évaluation of spectral angle mapper and spectral correlation mapper classifiers over multiple remote sensor data. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT),  (pp. 1–6). IEEE.

    Google Scholar 

  50. Petropoulos, G. P., Vadrevu, K. P., Xanthopoulos, G., Karantounias, G., & Scholze, M. (2010). A comparison of spectral angle mapper and artificial neural network classifiers combined with Landsat TM imagery analysis for obtaining burnt area mapping. Sensors, 10(3), 1967–1985.

    Article  Google Scholar 

  51. Hamida, A. B., Benoit, A., Lambert, P., & Amar, C. B. (2018). 3-D Deep learning approach for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing.

    Google Scholar 

  52. Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., … & Balling, R. C. (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sensing of Environment, 205, 253–275.

    Article  Google Scholar 

  53. Sahiner, B., Chan, H. P., Petrick, N., Wei, D., Helvie, M. A., Adler, D. D., et al. (1996). Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images. IEEE Transactions on Medical Imaging, 15(5), 598–610.

    Article  Google Scholar 

  54. Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98–113.

    Article  Google Scholar 

  55. Zou, Q., Ni, L., Zhang, T., & Wang, Q. (2015). Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters 12(11), 2321–2325.

    Article  Google Scholar 

  56. Zhao, W., & Du, S. (2016). Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 54(8), 4544–4554.

    Article  Google Scholar 

  57. Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782.

    Article  Google Scholar 

  58. Arkeman, Y., Buono, A., & Hermadi, I. (2017, January). Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm. In IOP Conference Series: Earth and Environmental Science (Vol. 54, No. 1, p. 012102). IOP Publishing.

    Google Scholar 

  59. Bergado, J. R., Persello, C., & Stein, A. (2018). Recurrent multiresolution convolutional networks for VHR image classification. arXiv preprint arXiv:1806.05793.

  60. Duarte, D., Nex, F., Kerle, N., & Vosselman, G. (2018). Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4(2).

    Google Scholar 

  61. Zhang, C., Pan, X., Li, H., Gardiner, A., Sargent, I., Hare, J., et al. (2018). A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 133–144.

    Article  Google Scholar 

  62. Breiman, L. (1996). Stacked regressions. Machine learning, 24(1), 49–64.

    Google Scholar 

  63. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  64. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).

    Google Scholar 

  65. Zhong, Z., Li, J., Ma, L., Jiang, H., & Zhao, H. (2017, July). Deep residual networks for hyperspectral image classification. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1824–1827). IEEE.

    Google Scholar 

  66. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017, July). Densely connected convolutional networks. In CVPR (Vol. 1, No. 2, p. 3).

    Google Scholar 

  67. Kanellopoulos, I., Wilkinson, G. G., Roli, F., & Austin, J. (Eds.). (2012). Neurocomputation in remote sensing data analysis: Proceedings of concerted action COMPARES (connectionist methods for pre-processing and analysis of remote sensing data). Springer Science & Business Media.

    Google Scholar 

  68. Waske, B., & Benediktsson, J. A. (2007). Fusion of support vector machines for classification of multisensor data. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 3858–3866.

    Article  Google Scholar 

  69. Salah, M. (2017). A survey of modern classification techniques in remote sensing for improved image classification. Journal of Geomatics, 11(1), 21.

    Google Scholar 

  70. Crnojević, V., Lugonja, P., Brkljač, B. N., & Brunet, B. (2014). Classification of small agricultural fields using combined Landsat-8 and RapidEye imagery: Case study of Northern Serbia. Journal of Applied Remote Sensing, 8(1), 083512.

    Article  Google Scholar 

  71. 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 

  72. Jin, W., Gong, F., Zeng, X., & Fu, R. (2016). Classification of clouds in satellite imagery using adaptive fuzzy sparse representation. Sensors, 16(12), 2153.

    Article  Google Scholar 

  73. Zhu, X., Ghahramani, Z., & Lafferty, J. D. (2003). Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International conference on Machine learning (ICML-03) (pp. 912–919).

    Google Scholar 

  74. Zhu, X. (2006). Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison, 2(3), 4–60.

    Google Scholar 

  75. Zhu, X. (2011). Semi-supervised learning. In Encyclopedia of machine learning (pp. 892–897). Boston, MA: Springer.

    Google Scholar 

  76. Camps-Valls, G., Marsheva, T. V. B., & Zhou, D. (2007). Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3044–3054.

    Article  Google Scholar 

  77. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38.

    MathSciNet  MATH  Google Scholar 

  78. Jackson, Q., & Landgrebe, D. A. (2001). An adaptive classifier design for high-dimensional data analysis with a limited training data set. IEEE Transactions on Geoscience and Remote Sensing, 39(12), 2664–2679.

    Article  Google Scholar 

  79. Vapnik, V. (1998). Statistical learning theory (Vol. 3). New York: Wiley.

    Google Scholar 

  80. Bruzzone, L., Chi, M., & Marconcini, M. (2005, July). Transductive SVMs for semi supervised classification of hyperspectral data. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 (Vol. 1, pp. 4-pp). IEEE.

    Google Scholar 

  81. Chung, F. R. (1996). Spectral graph theory (CBMS regional conference series in mathematics, No. 92).

    Google Scholar 

  82. Jordan, M. I., Sejnowski, T. J., & Poggio, T. A. (Eds.). (2001). Graphical models: Foundations of neural computation. MIT Press.

    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 Classification. 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_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6424-2_4

  • 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