La Rosa, L. E. C., M. Zortea, B. H. Gemignani, D. A. B. Oliveira, and R. Q. Feitosa. “FCRN-Based Multi-Task Learning for Automatic Citrus Tree Detection From UAV Images.” In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), pp. 403-408. IEEE, 2020.
2020
Preface: Technical Commission I
Hinz, S., R. Q. Feitosa, M. Weinmann, and B. Jutzi. “Preface: Technical Commission I.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 43 (2020): 7.
Learning Geometric Features for Improving the Automatic Detection of Citrus Plantation Rows in UAV Images
La Rosa, Laura Elena Cué, Dário AB Oliveira, Maciel Zortea, Bruno Holtz Gemignani, and Raul Queiroz Feitosa. “Learning Geometric Features for Improving the Automatic Detection of Citrus Plantation Rows in UAV Images.” IEEE Geoscience and Remote Sensing Letters (2020).
Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data
Sothe, C., C. M. De Almeida, M. B. Schimalski, L. E. C. La Rosa, J. D. B. Castro, R. Q. Feitosa, M. Dalponte et al. “Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data.” GIScience & Remote Sensing 57, no. 3 (2020): 369-394.
A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery
Soares, M.D., Dutra, L.V., da Costa, G.A.O.P., Feitosa, R.Q., Negri, R.G. and Diaz, P., 2020. A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery. Remote Sensing, 12(6), p.961.
A comparison of machine and deep-learning algorithms applied to multisource data for a subtropical forest area classification
Sothe, C., De Almeida, C.M., Schimalski, M.B., Liesenberg, V., La Rosa, L.E.C., Castro, J.D.B. and Feitosa, R.Q., 2020. A comparison of machine and deep-learning algorithms applied to multisource data for a subtropical forest area classification. International Journal of Remote Sensing, 41(5), pp.1943-1969.
Atrous cGAN for SAR to Optical Image Translation
J. N. Turnes, J. D. B. Castro, D. L. Torres, P. J. S. Vega, R. Q. Feitosa and P. N. Happ, “Atrous cGAN for SAR to Optical Image Translation,” in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2020.3031199.
First Results Of The LEM Benchmark Database For Agricultural Applications
Sanches, I. D., Feitosa, R. Q., Montibeller, B., Diaz, P. A., Luiz, A. J. B., Soares, M. D., … & Chamorro, J. (2020). First Results Of The LEM Benchmark Database For Agricultural Applications. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 251-256.
Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery
Lobo Torres, D.; Queiroz Feitosa, R.; Nigri Happ, P.; Elena Cué La Rosa, L.; Marcato Junior, J.; Martins, J.; Olã Bressan, P.; Gonçalves, W.N.; Liesenberg, V. Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery. Sensors 2020, 20, 563.
EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON
Andrade, R. B., Costa, G. A. O. P., Mota, G. L. A., Ortega, M. X., Feitosa, R. Q., Soto, P. J., and Heipke, C.: EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1497–1505, 2020.
DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST
Soto, P. J., Costa, G. A. O. P., Feitosa, R. Q., Happ, P. N., Ortega, M. X., Noa, J., Almeida, C. A., and Heipke, C.: DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1635–1643, 2020.
Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery
Ortega, M. X., Feitosa, R. Q., Happ, P. N., De Almeida, C., &, Gomes, A. (2020, March). Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery. Special Issue Assessing Changes in the Amazon and Cerrado Biomes by Remote Sensing.