Relative performance evaluation of similar supervised classification methods implemented in different geoinformation systems: impacts on the users’ interpretation

Arthur Duarte Vieira, João Vitor Meza Bravo, Vinicius Francisco Rofatto, George Deroco Martins

Abstract


In this paper we evaluated the performance of similar algorithms implemented in different geoinformation systems, in order to verify if there are differences among the results of the image classification processes, according to the platform used. We have selected two geoinformation systems commonly used for this purpose: SAGA GIS and ENVI. We have idealized a scenario where a user without formal education in Remote Sensing techniques carried the classification and interpreted the results. Therefore, we have evaluated the impacts of such analyses, which possibly would lead to mistakes. For this reason, we have given equivalent conditions for all algorithms and the systems selected, i.e., we have used the default parameters, for simulating an operator without formal education in Remote Sensing. In fact, the results showed that the same algorithm implemented in different software, generates different results, which impacts directly on the user’s interpretation. Therefore, it was confirmed that there must be attention while selecting a geoinformation system and an algorithm in order to perform an image classification. Otherwise, there will be inequivalent results among the software, which impacts on the user’s capacity of interpretation.


Keywords


Remote sensing; Image classification; Geoinformation systems; Performance analysis; Low cost.

References


AKAIKE, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974; 19 (6): 716–723.

AL-AHMADI, F. S., & HAMES, A. S. Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, kingdom of Saudi Arabia. Earth. 2009;20(1):167-191.

BARR, R. S., GOLDEN, B. L., KELLY, J., STEWARD, W., & RESENDE, M. Guidelines for designing and reporting on computational experiments with heuristic methods of the Proceedings of International conference on Metaheuristics for Optimization; 2001:1-17.

BERNARDI, H., DZEDZEJ, M., CARVALHO, L., & ACERBI JÚNIOR, F. W. 2007. Classificação digital do uso do solo comparando os métodos “pixel a pixel” e orientada ao objeto em imagem QuickBird of the Simpósio Brasileiro de Sensoriamento Remoto, 2007, Florianópolis, INPE:5595-5602.

CAMPOS, W. W., GASPAR, J., LAGE, M. O., KAWASHIMA, R. S., GIANNOTTI, M. A., & QUINTANILHA, J. A. Avaliação de classificadores de imagem de satélite a partir do uso de uma técnica de votação. Revista Brasileira de Cartografia. 2016;68 (8).

CANTY, M.J. Image analysis, classification and change detection in remote sensing with algorithms for ENVI/IDL: Taylor & Francis, CRC Press; 2006.

CENTENO, J. A. S. Sensoriamento remoto e processamento de imagens digitais. Curitiba: UFPR; 2003.

CRÓSTRA, A. Processamento digital de imagens de sensoriamento remoto. Campinas: Instituto de Geografia–UNICAMP; 1992.

DUARTE, D. C. O., ZANETTI, J., JUNIOR, J. G., & MEDEIROS, N. G. Comparison of supervised classification methods of Maximum Likelihood, Minimum Distance, Parallelepiped and Neural Network in images of Unmanned Air Vehicle (UAV) in Viçosa-MG. Revista Brasileira de Cartografia. 2018;70 (2).

ELWOOD, S.; GOODCHILD, M. F.; SUI, D. Z. Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice of the Association of American Geographers; 2012; 102(3):571-590.

ERBEK, F. S., ÖZKAN, C., & TABERNER, M. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing. 2004;25(9):1733-1748.

FONTE, C.C.; MINGHINI, M., PATRIARCA, J., ANTONIOU, V., SEE, L. & SKOPELITI, A. Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30. ISPRS International Journal of Geo-Information. 2017; vol. 6.

GREENBERG, H. J. Computational testing: Why, how and how much. ORSA Journal on Computing, 1990;2(1):94-97.

GRIFFIN, A. L.; WHITE, T.; FISH, C. TOMIO, B.; HUANG, H.; SLUTER, C. R et,. al. Designing across map use contexts: A research agenda. International Journal of Cartography. 2017;3:1-25.

HARKEN, J., & SUGUMARAN, R. Classification of Iowa wetlands using an airborne hyperspectral image: a comparison of the spectral angle mapper classifier and an object-oriented approach. Canadian Journal of remote sensing. 2005;31(2):167-174.

HEIPKE, C. Crowdsourcing Geospatial Data. ISPRS Journal of Photogrammetry and Remote Sensing. 2010;65(6):550-557.

KHATAMI, R., MONTRAKIS, 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 Sensing of Environment. 2016; vol. 177:89-100.

KRUSE, F. A., LEFKOFF, A. B., BOARDMAN, J. W., HEIDEBRECHT, K. B., SHAPIRO, A. T., BARLOON, P. J et., al. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote sensing of environment. 1993;44 (2-3):145-163.

LAMPARELLI, R. A. C., ROCHA, J. V., & BORGHI, E. Geoprocessamento e agricultura de precisao: fundamentos e aplicacoes. 2001:vol. 2.

LILLESAND, T. M., KIEFER, R. W., & CHIPMAN, J. W. Remote Sensing and Image Interpretation. New York: JohnWiley and Sons; 2004.

PERUMAL, K., & BHASKARAN, R. Supervised classification performance of multispectral images. Journal of Computing. 2010;Volume 2:Issue 2.

PINHO, C. D., FEITOSA, F. D. F., & KUX, H. 2005. Classificação automática de cobertura do solo urbano em imagem IKONOS: Comparação entre a abordagem pixel-a-pixel e orientada a objetos of the Simpósio Brasileiro de Sensoriamento Remoto; 2005; Goiânia, GO. INPE; p. 4217-4224.

RICHARDS, J. A. Remote Sensing Digital Image Analysis: An Introduction. Australia: Springer; 2012.

SEE, L.; MOONEY, P.; FOODY, G.; BASTIN, L.; COMBER, A.; ESTIMA, J. et., al. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The current state of Crowdsourced Geographic Information. ISPRS International Journal of Geo-Information. 2016;5(55):1-23.

SMITS, P. C., DELLEPIANE, S. G., & SCHOWENGERDT, R. A. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International journal of remote sensing. 1999;20 (8):1461-1486.

STEIN, A., VAN DER MEER, F. D., & GORTE, B. Spatial statistics for remote sensing: Springer Science & Business Media; 2006.




DOI: https://doi.org/10.5902/2179460X40068

Copyright (c) 2020 Ciência e Natura

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

DEAR AUTHORS,

PLEASE, CHECK CAREFULLY BEFORE YOUR SUBMISSION:

1. IF ALL AUTHORS "METADATA" (ORCID, LINK TO LATTES, SHORT BIOGRAPHY, AFFILIATION) WERE ADDED,

2. THE CORRECT IDIOM YOUR SECTION,

3 IF THE HIGHLIGHTS WERE ADDED,

4. IF THE GRAPHIC ABSTRACTS WAS ADDED,

5. IF THE REVIEWERS INDICATION WAS DONE,

6. IF THE REFERENCES FORMAT ARE CORRECT(ABNT)

7. IF THE RESOLUTION YOUR FIGURES (600 DPI) ARE SUITABLE

8.  IF THE STATEMENT BY THE ETHICS COMMITTEE (IF IT INVOLVES HUMANS) WAS ADDED;

9. IF THE DECLARATION OF ORIGINALITY WAS ADDED.

10. IF THE TEXT IS ORIGINAL. IF THE IDEA HAS ALREADY BEEN REGISTERED IN SUMMARY FORM, OR PUBLISHED IN CONGRESS ANNUALS, PLEASE INFORM THE EDITOR.