Analysis of meta-characteristics for land use and land cover classification using Random Forests
Abstract
This work analyzes the impact of the use of meta-features generated by the TWDTW algorithm for mapping land use and cover using Random Forest. The tests were carried out by classifying nine classes, for a set of samples from Mato Grosso region, in Brazil. Our results show that the meta-features are promising for the improvement of accuracy, increasing the overall accuracy of the tested models. The most significant improvements occur in the producer accuracy of the classes more difficult to classify. The importance of meta-features in the classification was significantly greater than features extracted from the EVI and NDVI indices and NIR and MIR bands.
Keywords:
TWDTW algorithm, mapping land use and cover, Random Forest
References
Ayala-Izurieta, J. E., Márquez, C. O., García, V. J., Recalde-Moreno, C. G., Rodríguez- Llerena, M. V., and Damián-Carrión, D. A. (2017). Land cover classification in an ecuadorian mountain geosystem using a random forest classifier, spectral vegetation indices, and ancillary geographic data. Geosciences, 7(2):34.
Bala, G., Caldeira, K., Wickett, M., Phillips, T., Lobell, D., Delire, C., and Mirin, A. (2007). Combined climate and carbon-cycle effects of large-scale deforestation. Proceedings of the National Academy of Sciences, 104(16):6550–6555.
Belgiu, M. and Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote sensing of environment, 204:509–523.
Belgiu, M. and Dragut¸, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114:24–31.
Bonan, G. B. (2008). Forests and climate change: forcings, feedbacks, and the climate benefits of forests. science, 320(5882):1444–1449.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32. Canovas-Garcia, F. and Alonso-Sarria, F. (2015). Optimal combination of classification algorithms and feature ranking methods for object-based classification of submeter resolution z/i-imaging dmc imagery. Remote Sensing, 7(4):4651–4677.
Dadi, M. M. (2019). Assessing the transferability of random forest and time-weighted dynamic time warping for agriculture mapping. Master’s thesis, University of Twente, Enschede.
Duro, D. C., Franklin, S. E., and Dub´e, M. G. (2012). Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing, 33(14):4502–4526.
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1):185–201.
Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Kalogirou, S., and Wolff, E. (2018). Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. GIScience & remote sensing, 55(2):221–242.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4):294–300.
Laliberte, A. S., Browning, D., and Rango, A. (2012). A comparison of three feature selection methods for object-based classification of sub-decimeter resolution ultracaml imagery. International Journal of Applied Earth Observation and Geoinformation, 15:70–78.
Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS International Journal of Geo-Information, 6(2):51.
Maus, V., Cˆamara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., and De Queiroz, G. R. (2016). A time-weighted dynamic time warping method for land-use and land-cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3729–3739.
Mountrakis, G., Im, J., and Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247–259.
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., da Motta, R. S., et al. (2014). Slowing amazon deforestation through public policy and interventions in beef and soy supply chains. science, 344(6188):1118–1123.
Oliveira, S. S., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. (2019). Exploiting parallelism to generate meta-features for land use and land cover classification with remote sensing time series. Brazilian Symposium on Geoinformatics (GeoInfo), pages 135–146.
Oliveira, S. S., Pascoal, L. M., Ferreira, L., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. (2018). Sp-twdtw: A new parallel algorithm for spatio-temporal analysis of remote sensing images. Brazilian Symposium on Geoinformatics (GeoInfo), pages 46–57.
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1):217–222.
Parente, L. and Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the brazilian pasturelands based on the automated classification of modis images from 2000 to 2016. Remote Sensing, 10(4):606.
Parente, L., Mesquita, V., Miziara, F., Baumann, L., and Ferreira, L. (2019). Assessing the pasturelands and livestock dynamics in brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and google earth engine cloud computing. Remote Sensing of Environment, 232:111301.
Picoli, M. C. A., Camara, G., Sanches, I., Sim˜oes, R., Carvalho, A., Maciel, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R. A., et al. (2018). Big earth observation time series analysis for monitoring brazilian agriculture. ISPRS journal of photogrammetry and remote sensing, 145:328–339.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for landcover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67:93– 104.
Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., and Shi, L. (2018). Mapping vegetation and land use types in fanjingshan national nature reserve using google earth engine. Remote Sensing, 10(6):927.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer New York. Wiens, T. S., Dale, B. C., Boyce, M. S., and Kershaw, G. P. (2008). Three way k-fold cross-validation of resource selection functions. Ecological Modelling, 212(3-4):244– 255.
Bala, G., Caldeira, K., Wickett, M., Phillips, T., Lobell, D., Delire, C., and Mirin, A. (2007). Combined climate and carbon-cycle effects of large-scale deforestation. Proceedings of the National Academy of Sciences, 104(16):6550–6555.
Belgiu, M. and Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote sensing of environment, 204:509–523.
Belgiu, M. and Dragut¸, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114:24–31.
Bonan, G. B. (2008). Forests and climate change: forcings, feedbacks, and the climate benefits of forests. science, 320(5882):1444–1449.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32. Canovas-Garcia, F. and Alonso-Sarria, F. (2015). Optimal combination of classification algorithms and feature ranking methods for object-based classification of submeter resolution z/i-imaging dmc imagery. Remote Sensing, 7(4):4651–4677.
Dadi, M. M. (2019). Assessing the transferability of random forest and time-weighted dynamic time warping for agriculture mapping. Master’s thesis, University of Twente, Enschede.
Duro, D. C., Franklin, S. E., and Dub´e, M. G. (2012). Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing, 33(14):4502–4526.
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1):185–201.
Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Kalogirou, S., and Wolff, E. (2018). Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. GIScience & remote sensing, 55(2):221–242.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R. (2006). Random forests for land cover classification. Pattern Recognition Letters, 27(4):294–300.
Laliberte, A. S., Browning, D., and Rango, A. (2012). A comparison of three feature selection methods for object-based classification of sub-decimeter resolution ultracaml imagery. International Journal of Applied Earth Observation and Geoinformation, 15:70–78.
Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., Ma, X., and Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS International Journal of Geo-Information, 6(2):51.
Maus, V., Cˆamara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., and De Queiroz, G. R. (2016). A time-weighted dynamic time warping method for land-use and land-cover mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3729–3739.
Mountrakis, G., Im, J., and Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247–259.
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., da Motta, R. S., et al. (2014). Slowing amazon deforestation through public policy and interventions in beef and soy supply chains. science, 344(6188):1118–1123.
Oliveira, S. S., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. (2019). Exploiting parallelism to generate meta-features for land use and land cover classification with remote sensing time series. Brazilian Symposium on Geoinformatics (GeoInfo), pages 135–146.
Oliveira, S. S., Pascoal, L. M., Ferreira, L., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. (2018). Sp-twdtw: A new parallel algorithm for spatio-temporal analysis of remote sensing images. Brazilian Symposium on Geoinformatics (GeoInfo), pages 46–57.
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1):217–222.
Parente, L. and Ferreira, L. (2018). Assessing the spatial and occupation dynamics of the brazilian pasturelands based on the automated classification of modis images from 2000 to 2016. Remote Sensing, 10(4):606.
Parente, L., Mesquita, V., Miziara, F., Baumann, L., and Ferreira, L. (2019). Assessing the pasturelands and livestock dynamics in brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and google earth engine cloud computing. Remote Sensing of Environment, 232:111301.
Picoli, M. C. A., Camara, G., Sanches, I., Sim˜oes, R., Carvalho, A., Maciel, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R. A., et al. (2018). Big earth observation time series analysis for monitoring brazilian agriculture. ISPRS journal of photogrammetry and remote sensing, 145:328–339.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for landcover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67:93– 104.
Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., and Shi, L. (2018). Mapping vegetation and land use types in fanjingshan national nature reserve using google earth engine. Remote Sensing, 10(6):927.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer New York. Wiens, T. S., Dale, B. C., Boyce, M. S., and Kershaw, G. P. (2008). Three way k-fold cross-validation of resource selection functions. Ecological Modelling, 212(3-4):244– 255.
Published
2020-06-30
How to Cite
PAIVA, Roberto; OLIVEIRA, Sávio; MARTINS, Wellington; PARENTE, Leandro.
Analysis of meta-characteristics for land use and land cover classification using Random Forests. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 11. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 71-80.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2020.11021.
