Tucumã: A toolbox for spatiotemporal remote sensing image analysis [software and data sets, IEEE Geoscience and Remote Sensing Magazine, vol.7, issue.3, pp.110-122, 2019. ,
Remote sensing image classification using genetic-programming-based time series similarity functions, IEEE Geoscience and Remote Sensing Letters, vol.14, issue.9, pp.1499-1503, 2017. ,
Time series-based classifier fusion for fine-grained plant species recognition, Pattern Recognition Letters, vol.81, pp.101-109, 2016. ,
Fusion of time series representations for plant recognition in phenology studies, Pattern Recognition Letters, vol.83, issue.2, pp.205-214, 2016. ,
Applying machine learning based on multiscale classifiers to detect remote phenology patterns in cerrado savanna trees, Ecological Informatics, vol.23, issue.0, pp.49-61, 2014. ,
Unsupervised distance learning for plant species identification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.12, pp.5325-5338, 2016. ,
Phenological visual rhythms: Compact representations for fine-grained plant species identification, Pattern Recognition Letters, vol.81, pp.90-100, 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, vol.9, issue.8, pp.3729-3739, 2016. ,
Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil, RSE, vol.152, pp.136-149, 2014. ,
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Min. Knowl. Discov, vol.31, issue.3, pp.606-660, 2017. ,
Experimental comparison of representation methods and distance measures for time series data, Data Min. Knowl. Discov, vol.26, issue.2, pp.275-309, 2013. ,
Imaging time-series to improve classification and imputation, Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI'2015), pp.3939-3945, 2015. ,
Recurrence plots of dynamical systems, World Scientific Series on Nonlinear Science Series A, vol.16, pp.441-446, 1995. ,
Pixelwise remote sensing image classification based on recurrence plot deep features, IEEE International Geoscience and Remote Sensing Symposium, pp.1310-1313, 2019. ,
Image-based time series representations for pixelwise eucalyptus region classification: A comparative study, IEEE Geoscience and Remote Sensing Letters, pp.1-5, 2020. ,
URL : https://hal.archives-ouvertes.fr/hal-02912924
A soft computing framework for image classification based on recurrence plots, IEEE Geoscience and Remote Sensing Letters, vol.16, issue.2, pp.320-324, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02627927
An information retrieval approach for large-scale time series retrieval, IEEE International Geoscience and Remote Sensing Symposium, pp.254-257, 2019. ,
Densely connected convolutional networks, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4700-4708 ,
Inception-v4, inception-resnet and the impact of residual connections on learning, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp.4278-4284 ,
Rethinking the inception architecture for computer vision, Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR'2016), pp.2818-2826, 2016. ,
Mobilenets: Efficient convolutional neural networks for mobile vision applications, vol.04, p.2017 ,
Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR'2016, pp.770-778, 2016. ,
Very deep convolutional networks for large-scale image recognition, Proceedings of the International Conference on Learning Representations (ICLR'2015), 2014. ,
Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1251-1258 ,
Classification and regression trees -l. breiman, j. h. friedman, r. a. olshen and c. j. stone, Metrika, vol.33, pp.128-128, 1986. ,
The balanced accuracy and its posterior distribution, 2010 20th International Conference on Pattern Recognition, pp.3121-3124, 2010. ,
A coefficient of agreement for nominal scales, Educational and Psychological Measurement, vol.20, issue.1, pp.37-46, 1960. ,
Recurrence plots of experimental data: To embed or not to embed?, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.8, issue.4, pp.861-871, 1998. ,
Deep learning in remote sensing applications: A meta-analysis and review, ISPRS journal of photogrammetry and remote sensing, vol.152, pp.166-177, 2019. ,
A phenology-based spectral and temporal feature selection method for crop mapping from satellite time series, International Journal of Applied Earth Observation and Geoinformation, vol.80, pp.218-229, 2019. ,
Unvi-based time series for vegetation discrimination using separability analysis and random forest classification, Remote Sensing, vol.12, issue.3, p.529, 2020. ,
A new vegetation index based on the universal pattern decomposition method, International Journal of Remote Sensing, vol.28, issue.1, pp.107-124, 2007. ,
Crop type identification and mapping using machine learning algorithms and sentinel-2 time series data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.12, issue.9, pp.3295-3306, 2019. ,
Forest structure parameter extraction using spot-7 satellite data by objectand pixel-based classification methods, Environmental Monitoring and Assessment, vol.192, issue.1, p.43, 2020. ,
Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation, Ecological Informatics, vol.19, issue.0, pp.62-70, 2014. ,
Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation, Perspectives in Ecology and Conservation, vol.15, issue.2, pp.82-90, 2017. ,
Deriving vegetation indices for phenology analysis using genetic programming, Ecological Informatics, vol.26, pp.61-69, 2015. ,
A framework for selection and fusion of pattern classifiers in multimedia recognition, Pattern Recognition and Computer Vision, vol.39, pp.52-64, 2014. ,
Ensemble of multi-view learning classifiers for cross-domain iris presentation attack detection, IEEE Transactions on Information Forensics and Security, vol.14, issue.6, pp.1419-1431, 2018. ,
The measurement of observer agreement for categorical data, Biometrics, vol.33, issue.1, 1977. ,
A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci, vol.55, issue.1, pp.119-139, 1997. ,
, Pattern Recognition and Machine Learning (Information Science and Statistics, 2006.
Greedy function approximation: A gradient boosting machine, The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2001. ,
Exploiting convnet diversity for flooding identification, IEEE Geoscience and Remote Sensing Letters, vol.15, issue.9, pp.1446-1450, 2018. ,