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Assessing Satellite Image Time Series Clustering Using Growing SOM

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12253))

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Abstract

Mapping Earth land use and cover changes is crucial to understand agricultural dynamics. Recently, analysis of time series extracted from Earth observation satellite images has been widely used to produce land use and cover information. In time series analysis, clustering is a common technique performed to discover patterns on data sets. In this work, we evaluate the Growing Self-Organizing Maps algorithm for clustering satellite image time series and compare it with Self-Organizing Maps algorithm. This paper presents a case study using satellite image time series associated to samples of land use and cover classes, highlighting the advantage of providing a neutral factor (called spread factor) as a parameter for GSOM, instead of the SOM grid size.

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References

  1. Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans. Neural Netw. 11, 601–614 (2000)

    Article  Google Scholar 

  2. Bagan, H., Wang, Q., Watanabe, M., Yang, Y., Ma, J.: Land cover classification From Modis EVI time-series data using SOM neural network. Int. J. Remote Sens. 2, 4999–5012 (2005)

    Article  Google Scholar 

  3. Eddelbuettel, D., François, R.: Rcpp: seamless R and C++ integration. J. Stat. Softw. 40, 1–18 (2011)

    Google Scholar 

  4. Ferreira, K.R., Santos, L., Picoli, M.C.A.: Evaluating distance measures for image time series clustering in land use and cover monitoring. In: Machine Learning for Earth Observation Workshop (2019)

    Google Scholar 

  5. Flexer, A.: On the use of self-organizing maps for clustering and visualization. J. Intell. Data Anal. 5, 373–384 (2001)

    Article  Google Scholar 

  6. Gomez, C., White, J.C., Wulder, M.A.: Optical remotely sensed time series data for land cover classification: a review. ISPRS J. Photogramm. Remote Sens. 116, 55–72 (2016)

    Article  Google Scholar 

  7. GSOM Python Implementation Repository. https://github.com/rodrigosales/GSOM. Accessed 3 May 2020

  8. Kohonen, T., Schroeder, M.R., Huang, T.S.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-642-56927-2

    Book  MATH  Google Scholar 

  9. Natita, W., Wiboonsak, W., Dusadee, S.: Appropriate learning rate and neighborhood function of self-organizing map (SOM) for specific humidity pattern classification over Southern Thailand. Int. J. Model. Optim. 6, 61 (2016)

    Article  Google Scholar 

  10. Pasquarella, V.J., Holden, C.E., Kaufman, L., Woodcock, C.E.: From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics. Remote Sens. Ecol. Conserv. 2, 152–170 (2016)

    Article  Google Scholar 

  11. Picoli, M., et al.: Big earth observation time series analysis for monitoring Brazilian agriculture. ISPRS J. Photogramm. Remote Sens. 145, 328–339 (2018)

    Article  Google Scholar 

  12. Python Software Foundation. Python Language Reference, version 3.7.2. http://www.python.org

  13. Santos, L., Ferreira, K.R., Picoli, M., Camara, G.: Self-organizing maps in earth observation data cubes analysis. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J.D. (eds.) WSOM 2019. AISC, vol. 976, pp. 70–79. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-19642-4_7

    Chapter  Google Scholar 

  14. Vasighi, M., Abbasi, S.: Multiple growing self-organizing map for data classification. In: International Symposium on Artificial Intelligence and Signal Processing (2017)

    Google Scholar 

  15. Wehrens, R., Buydens, L.: Self and Super-Organizing Maps in R: the Kohonen Package. J. Stat. Softw. 21, 1–19 (2007)

    Article  Google Scholar 

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Correspondence to Rodrigo de Sales da Silva Adeu .

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da Silva Adeu, R.d.S., Ferreira, K.R., Andrade, P.R., Santos, L. (2020). Assessing Satellite Image Time Series Clustering Using Growing SOM. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_19

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  • DOI: https://doi.org/10.1007/978-3-030-58814-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58813-7

  • Online ISBN: 978-3-030-58814-4

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