Next Article in Journal
Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images
Next Article in Special Issue
A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications
Previous Article in Journal
Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources
Previous Article in Special Issue
The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities
 
 
Article

Satellite Image Time Series Analysis for Big Earth Observation Data

1
National Institute for Space Research (INPE), Avenida dos Astronautas, 1758, Jardim da Granja, Sao Jose dos Campos, SP 12227-010, Brazil
2
National Institute for Applied Economics Research, SBS, Quadra 1 Bloco J, Brasília, DF 70076-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Pieter Kempeneers
Remote Sens. 2021, 13(13), 2428; https://doi.org/10.3390/rs13132428
Received: 29 April 2021 / Revised: 8 June 2021 / Accepted: 9 June 2021 / Published: 22 June 2021
The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018. View Full-Text
Keywords: big Earth observation data; data cubes; satellite image time series; machine learning and deep learning for remote sensing; R package big Earth observation data; data cubes; satellite image time series; machine learning and deep learning for remote sensing; R package
Show Figures

Figure 1

MDPI and ACS Style

Simoes, R.; Camara, G.; Queiroz, G.; Souza, F.; Andrade, P.R.; Santos, L.; Carvalho, A.; Ferreira, K. Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sens. 2021, 13, 2428. https://doi.org/10.3390/rs13132428

AMA Style

Simoes R, Camara G, Queiroz G, Souza F, Andrade PR, Santos L, Carvalho A, Ferreira K. Satellite Image Time Series Analysis for Big Earth Observation Data. Remote Sensing. 2021; 13(13):2428. https://doi.org/10.3390/rs13132428

Chicago/Turabian Style

Simoes, Rolf, Gilberto Camara, Gilberto Queiroz, Felipe Souza, Pedro R. Andrade, Lorena Santos, Alexandre Carvalho, and Karine Ferreira. 2021. "Satellite Image Time Series Analysis for Big Earth Observation Data" Remote Sensing 13, no. 13: 2428. https://doi.org/10.3390/rs13132428

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop