Parallel Processing of Remote Sensing Time Series Applied to Land-Use and Land-Cover Classification
DOI:
https://doi.org/10.5753/jidm.2021.1785Keywords:
Classification, Land-Use and Land-Cover, Parallel Processing, Remote Sensing, Time SeriesAbstract
The increase in satellite launches into Earth's orbit in recent years has generated a huge amount of remote sensing data. These data, in the form of time series, have been used in automated classification approaches, generating land-use and land-cover (LULC) products for different landscapes around the world. Dynamic Time Warping (DTW) is a well-known computational method used to measure the similarity between time series. Tt has been used in many algorithms for remote sensing time series analysis. These DTW-based algorithms are capable of generating similarity measures between time series and patterns. These measures can be used as meta-features to increase the accuracy results of classification models. However, DTW-based algorithms require a lot of computational resources and have a high execution time, which makes them difficult to use in large volumes of data. This article presents a parallel and fully scalable solution to optimize the construction of meta-features through remote sensing time series (RSTS). In addition, results of the application of the generated meta-features in the training and evaluation of classification models using Random Forest are presented. The results show that the proposed approaches have led to improvements in execution time and accuracy when compared to traditional strategies.
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Ayala-Izurieta, J. E., Márquez, C. O., García, V. J., Recalde-Moreno, C. G., Rodríguez-Llerena, M. V., and Damián-Carrión, D. A. Land cover classification in an ecuadorian mountain geosystem using a random forest classifier, spectral vegetation indices, and ancillary geographic data. Geosciences 7 (2): 34, 2017.
Bala, G., Caldeira, K., Wickett, M., Phillips, T., Lobell, D., Delire, C., and Mirin, A. Combined climate and carbon-cycle effects of large-scale deforestation. Proceedings of the National Academy of Sciences 104 (16): 6550–6555, 2007.
Belgiu, M. and Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing vol. 114, pp. 24–31, 2016.
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320 (5882): 1444–1449, 2008.
Camara, G., Assis, L. F., Ribeiro, G., Ferreira, K. R., Llapa, E., and Vinhas, L. Big earth observation data analytics. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data - BigSpatial '16. ACM Press, 2016.
Dadi, M. M. Assessing the transferability of random forset and time-weighted dynamic time warping for agriculture mapping. M.S. thesis, University of Twente, 2019.
Dewan, A. M. and Yamaguchi, Y. Land use and land cover change in greater dhaka, bangladesh: Using remote sensing to promote sustainable urbanization. Applied geography 29 (3): 390–401, 2009.
Foody, G. M. Status of land cover classification accuracy assessment. Remote Sensing of Environment 80 (1): 185–201, 2002.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R. Random forests for land cover classification. Pattern Recognition Letters 27 (4): 294–300, 2006.
Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. Mapping rice cropping systems in vietnam using an ndvi-based time-series similarity measurement based on dtw distance. Remote Sensing 8 (1): 19, 2016.
Hansen, M. C. and Loveland, T. R. A review of large area monitoring of land cover change using landsat data. Remote Sensing of Environment vol. 122, pp. 66–74, July, 2012.
Houborg, R. and McCabe, M. F. A cubesat enabled spatio-temporal enhancement method (CESTEM) utilizing planet, landsat and MODIS data. Remote Sensing of Environment vol. 209, pp. 211–226, May, 2018.
Manabe, V. D., Melo, M. R., and Rocha, J. V. Framework for mapping integrated crop-livestock systems in mato grosso, brazil. Remote Sensing 10 (9): 1322, 2018.
Maus, V. Land Use and Land Cover Monitoring Using Remote Sensing Image Time Series. Ph.D. thesis, PhD thesis, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, 2016.
Maus, V., Camara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., and De Queiroz, G. R. 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, 2016.
Nepstad, D., McGrath, D., Stickler, C., Alencar, A., Azevedo, A., Swette, B., Bezerra, T., DiGiano, M., Shimada, J., da Motta, R. S., et al. Slowing amazon deforestation through public policy and interventions in beef and soy supply chains. science 344 (6188): 1118–1123, 2014.
NVIDIA. CUDA C++ Programming Guide. http://docs.nvidia.com/cuda/cuda-c-programming-guide/, 2020.
Oliveira, S. S., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. Exploiting parallelism to generate meta-features for land use and land cover classification with remote sensing time series. Brazilian Symposium on Geoinformatics (GeoInfo), 2019.
Oliveira, S. S., Pascoal, L. M., Ferreira, L., Cardoso, M. d. C., Bueno, E., Rodrigues, V. J., and Martins, W. S. Sp-twdtw: A new parallel algorithm for spatio-temporal analysis of remote sensing images. Brazilian Symposium on Geoinformatics (GeoInfo), 2018.
Oliveira, S. S. T., Martins, W. S., Sacramento, V., Bueno, E., Cardoso, M., and Pascoal, L. A parallel and distributed approach to the analysis of time series on remote sensing big data. Journal of Information and Data Management 10 (1): 16–34, 2019.
Paiva, R. U., Oliveira, S. S., Pascoal, L. M., Parente, L. L., and Martins, W. S. An efficient solution to generate meta-features for classification with remote sensing time series. Brazilian Symposium on Geoinformatics (GeoInfo), 2020.
Pal, M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing 26 (1): 217–222, 2005.
Parente, L. and Ferreira, L. 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, 2018.
Parente, L., Mesquita, V., Miziara, F., Baumann, L., and Ferreira, L. 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 vol. 232, pp. 111301, 2019.
Picoli, M. C. A., Camara, G., Sanches, I., Simoes, R., Carvalho, A., Maciel, A., Coutinho, A., Esquerdo, J., Antunes, J., Begotti, R. A., et al. Big earth observation time series analysis for monitoring brazilian agriculture. ISPRS journal of photogrammetry and remote sensing vol. 145, pp. 328–339, 2018.
Plaza, A. High performance computing in remote sensing. Chapman & Hall/CRC, Boca Raton, FL, 2008.
Rabiner, L., Rosenberg, A., and Levinson, S. Considerations in dynamic time warping algorithms for discrete word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26 (6): 575–582, 1978.
Romani, L. A., Goncalves, R., Zullo, J., Traina, C., and Traina, A. J. New dtw-based method to similarity search in sugar cane regions represented by climate and remote sensing time series. In 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 355–358, 2010.
Sakoe, H. and Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing 26 (1): 43–49, 1978.
Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L., and Shi, L. Mapping vegetation and land use types in fanjingshan national nature reserve using google earth engine. Remote Sensing 10 (6): 927, 2018.
