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.
Publications
MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE
Ayma, V., C. Beltrán, P. N. Happ, G. A. O. P. Costa, and R. Q. Feitosa. “MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences (2019).
Combining deep learning and prior knowledge for crop mapping in tropical regions from multitemporal SAR image sequences
Cué La Rosa, Laura Elena, Raul Queiroz Feitosa, Patrick Nigri Happ, Ieda Del’Arco Sanches, and Gilson Alexandre Ostwald Pedro da Costa. “Combining deep learning and prior knowledge for crop mapping in tropical regions from multitemporal SAR image sequences.” Remote Sensing 11, no. 17 (2019): 2029.
Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs
Santos, Anderson Aparecido dos, Jose Marcato Junior, Márcio Santos Araújo, David Robledo Di Martini, Everton Castelão Tetila, Henrique Lopes Siqueira, Camila Aoki et al. “Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs.” Sensors 19, no. 16 (2019): 3595.
A COMPARISON BETWEEN THE HADOOP AND SPARK DISTRIBUTED FRAMEWORKS IN THE CONTEXT OF REGION-GROWING SEGMENTATION OF REMOTE SENSING IMAGES
Andrade, R. B., J. M. F. Santos, G. A. O. P. Costa, G. L. A. Mota, P. N. Happ, and R. Q. Feitosa. “A COMPARISON BETWEEN THE HADOOP AND SPARK DISTRIBUTED FRAMEWORKS IN THE CONTEXT OF REGION-GROWING SEGMENTATION OF REMOTE SENSING IMAGES.” ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 4 (2019).
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).
IEEE GRSS Brazil Chapter: Status and Activities in 2019
Liesenberg, Veraldo, Jose Marcato, Raul Queiroz Feitosa, Alessandra Rodrigues Gomes, Jefersson Alex Dos Santos, Rafael Lemos Paes, Edson A. Mitishita, Antonio MG Tommaselli, Fatima N. Sombra de Sombra, and Alejandro C. Frery. “IEEE GRSS Brazil Chapter: Status and Activities in 2019 [Chapters].” IEEE Geoscience and Remote Sensing Magazine 8, no. 4 (2020): 144-151.
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.
Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences
Martinez, Jorge Andres Chamorro, Laura Elena Cué La Rosa, Raul Queiroz Feitosa, Ieda Del’Arco Sanches, and Patrick Nigri Happ. “Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences.” ISPRS Journal of Photogrammetry and Remote Sensing 171: 188-201.
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.
Semantic Segmentation Of Endangered Tree Species In Brazilian Savanna Using Deeplabv3+ Variants
D. L. Torres et al., “Semantic Segmentation Of Endangered Tree Species In Brazilian Savanna Using Deeplabv3+ Variants,” 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile, 2020, pp. 515-520, doi: 10.1109/LAGIRS48042.2020.9165625.
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.
A Many-To-Many Fully Convolutional Recurrent Network For Multitemporal Crop Recognition
Chamorro, J. A., Castro, J. B., Happ, P. N. & Feitosa, R. Q. (2019, September). A Many-To-Many Fully Convolutional Recurrent Network For Multitemporal Crop Recognition. PIA19+MRSS19 – Photogrammetric Image Analysis & Munich Remote Sensing Symposium 2019, Munich, Germany.
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.