Advertisement

Method for the Automated Generation of a Forest Non Forest Map with LANDSAT 8 Imagery by Using Artificial Neural Networks and the Identification of Pure Class Pixels

  • Juan-Carlos Tituana
  • Cindy-Pamela LopezEmail author
  • Sang Guun Yoo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

In this work, a methodology for the automated classification of Landsat 8 images from the integration of Artificial Neural Networks and the identification of pixels of pure classes is presented. The exercise carried out in this research by using the SEPAL platform, allowed to obtain a mosaic L8 of the study area, fully preprocessed and calibrated, and it was generated automatically in a short period of time. This result represents a significant advance in terms of preprocessing capacity that currently exists for the management of satellite data compared to the state of the area a decade ago. This relevant advance has been possible due to the use of artificial neural networks and the cross-correlation coefficient of the pixels of the Landsat 8 satellite platform images. Their use and differentiation of areas in remote sensing of wooded, agricultural and water areas are discussed.

Keywords

Artificial neural networks Forest non-forest map Cross correlation coefficient LANDSAT 8 imagery 

References

  1. 1.
    Belward, A.S., Skøien, J.O.: Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J. Photogram. Remote Sens. 103, 115–128 (2015)CrossRefGoogle Scholar
  2. 2.
    Hasmadi, M., Pakhriazad, H.Z., Shahrin, M.F.: Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geogr.-Malays. J. Soc. Space 5(1), 1–10 (2017)Google Scholar
  3. 3.
    Lary, D.J., Alavi, A.H., Gandomi, A.H., Walker, A.L.: Machine learning in geosciences and remote sensing. Geosci. Front. 7(1), 3–10 (2016)CrossRefGoogle Scholar
  4. 4.
    Bischof, H., Schneider, W., Pinz, A.J.: Multispectral classification of LANDSAT-images using neural networks. IEEE Trans. Geosci. Remote Sens. 30(5), 482–490 (1992)CrossRefGoogle Scholar
  5. 5.
    Kamata, S.-I., Eason, R.O., Perez, A., Kawaguchi, E.: A neural network classifier for LANDSAT image data. In: Proceedings 11th IAPR International Conference on Pattern Recognition, 1992, Conference B: Pattern Recognition Methodology and Systems, vol. II, pp. 573–576 (1992)Google Scholar
  6. 6.
    Salahova, S.: Remote sensing and GIS application for earth observation on the base of the neural networks in aerospace image classification. In: 2007 3rd International Conference on Recent Advances in Space Technologies RAST 2007, pp. 275–278 (2007)Google Scholar
  7. 7.
    Lee, J., Weger, R.C., Sengupta, S.K., Welch, R.M.: A neural network approach to cloud classification. IEEE Trans. Geosci. Remote Sens. 28(5), 846–855 (1990)CrossRefGoogle Scholar
  8. 8.
    Gao, Y., Zhang, W., Wang, J., Liu, C.: LULC classification of LANDSAT- 7 ETM+ image from rugged terrain using TC, CA and SOFM neural network. In: 2007 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2007, pp. 3490–3493 (2007)Google Scholar
  9. 9.
    Mas, J.-F.: An articial neural networks approach to map land use/cover using Landsat imagery and incillary data. In: 2003 Proceedings of IEEE International Geoscience and Remote Sensing Symposium 2003 IGARSS 2003, vol. 6, pp. 3498–3500 (2003)Google Scholar
  10. 10.
    Paola, J.D., Schowengerdt, R.A.: A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Trans. Geosci. Remote Sens. 33(4), 981–996 (1995)CrossRefGoogle Scholar
  11. 11.
    Chae, H.S., Kim, S.J., Ryu, J.A.: A classfication of multitemporal LANDSAT TM data using principal component analysis and artificial neural network. In:1997 IEEE International Geoscience and Remote Sensing, IGARSS 1997, vol. 1, pp. 517–520. Remote Sensing-A Scientific Vision for Sustainable Development (1997)Google Scholar
  12. 12.
    Lee, S., Lathrop, R.G.: Subpixel analysis of Landsat ETM/sup+/using self-organizing map (SOM) neural networks for urban land cover characterization. IEEE Trans. Geosci. Remote Sens. 44(6), 1642–1654 (2006)CrossRefGoogle Scholar
  13. 13.
    Yoshida, T., Omatu, S.: A remotely sensed data separation method with neural networks. In: IEEE 2001 International Geoscience and Remote Sensing Symposium, IGARSS 2001, vol. 7, pp. 3300–3302 (2001)Google Scholar
  14. 14.
    Foody, G.A., Cutler, M.E.: Remote sensing of biodiversity: using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from landsat TM data. In: 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2002, vol. 1, pp. 497–499 (2002)Google Scholar
  15. 15.
    Feldberg, I., Netanyahu, N.S., Shoshany, M.: A neural network-based technique for change detection of linear features and its application to a Mediterranean region. In: 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2002, vol 2, pp. 1195–1197 (2002)Google Scholar
  16. 16.
    Velloso, M.L.F., de Souza, F.J., Simoes, M.: Improved radiometric normalization for land cover change detection: an automated relative correction with artificial neural network. In: 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2002, vol. 6, pp. 3435–3437 (2002)Google Scholar
  17. 17.
    Fang, H., Liang, S.: Retrieving leaf area index with a neural network method: simulation and validation. IEEE Trans. Geosci. Remote. Sens. 41(9), 2052–2062 (2003)CrossRefGoogle Scholar
  18. 18.
    Zhao, D., Zhang, W., Shijin, X.: A neural network algorithm to retrieve near surface air temperature from landsat ETM+ imagery over the Hanjiang River Basin, China. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 1705–1708 (2007)Google Scholar
  19. 19.
    Neagoe, V., Strugaru, G: A concurrent neural network model for pattern recognition in multispectral satellite imagery. In: World Automation Congress, WAC 2008, pp. 1–6 (2008)Google Scholar
  20. 20.
    Solaiman, B., Mouchot, M.C., Koffi, R.K: Multispectral LANDSAT images segmentation using neural networks and multi-experts approach. In: International Geoscience and Remote Sensing Symposium, IGARSS 1994, vol. 4, pp. 2109–2111. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation (1994)Google Scholar
  21. 21.
    Munoz, E.A., Di Paola, F., Lanfri, M.A.: Advances on rain rate retrieval from satellite platforms using artificial neural networks. IEEE Lat. Am. Trans. 13(10), 3179–3186 (2015)CrossRefGoogle Scholar
  22. 22.
    Ministerio del Ambiente del Ecuador: Estadísticos de Patrimonio Natural, Publicaciones del MAE (2015)Google Scholar
  23. 23.
    Sepal-FAO.: System for earth observations, data access, processing & analysis for land monitoring (2018). https://sepal.io/. Accessed 30 May 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facultad de Ingeniería de SistemasEscuela Politécnica NacionalQuitoEcuador

Personalised recommendations