Ortega, M. X., Bermudez J.D, Happ, P. N., Gomes, A., & Feitosa, R. Q. (2019, September). Evaluation of Deep Learning techniques for deforestation detection in the Amazon Forest. PIA19+MRSS19 – Photogrammetric Image Analysis & Munich Remote Sensing Symposium 2019, Munich, Germany.
Soto, P. J., Bermudez, J. D., Happ, P. N., and Feitosa, R. Q.: A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 167–173, 2019.
BERMUDEZ, JOSE D.; HAPP, PATRICK N.; FEITOSA, RAUL Q.; OLIVEIRA, DARIO A. B. Synthesis of Multispectral Optical Images From SAR/Optical Multitemporal Data Using Conditional Generative Adversarial Networks. IEEE Geoscience and Remote Sensing Letters
Ferreira, P. M., La Rosa, L. E. C., Happ, P. N., Theobald, R. B. & Feitosa, R. Q. (2019, April). MAPPING EUCALYPTUS PLANTATIONS AND NATURAL FOREST AREAS IN LANDSAT-TM IMAGES USING DEEP LEARNING. XIX Simpósio Brasileiro de Sensoriamento Remoto 2019, Santos, SP.
Chamorro, J. A., Happ, P. N., Castro, J. B., La Rosa, L. E. C., & Feitosa, R. Q. (2019, April). A Comparison Of Fully Convolutional And Recurrent Networks For Multi-Temporal Crop Recognition Using SAR Images. XIX Brazilian Symposium on Remote Sensing 2019, Santos, SP.
Ortega, M. X., Happ, P. N., & Feitosa, R. Q. (2019, April). Assessment of an Early Fusion CNN Approach applied to the deforestation detection in the Brazilian Amazon. XIX Simpósio Brasileiro de Sensoriamento Remoto 2019, Santos, SP.
Ortega, M. X., Castro, J. B., Soto, P., & Feitosa, R. Q. (2018, November). Unsupervised Representation Learning for Classification of Remote Sensing images based on Generative Adversarial Networks. GRSS-YP & ISPRS Student Consortium SS 2018, Campo Grande (pp. 46).
Antunes, R. R., Blaschke, T., Tiede, D., Bias, E. S., Costa, G. A. O. P. and Happ, P. N.: Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification GIScience & Remote Sensing, 2018
Castro, J. B., Feitosa, R. Q., & Happ, P. N. (2018, July). An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 3824-3827). IEEE.
La Rosa, L. E. C., Happ, P. N., & Feitosa, R. Q. (2018, July). Dense Fully Convolutional Networks for Crop Recognition from Multitemporal SAR Image Sequences. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 7460-7463). IEEE.
Almeida, C.M.; Feitosa, R. Q.; Hernandez, J.; Scavuzzo, C. M. ; Aragão, L. E.O.C. Coping with environmental challenges in Latin America, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 145, Part B, 2018.
Bermudez, J. D., Happ, P. N., Oliveira, D. A. B., and Feitosa, R. Q.: SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS., ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 5-11, https://doi.org/10.5194/isprs-annals-IV-1-5-2018, 2018.
Sanches, I. D., Feitosa, R. Q., Achanccaray, P., Montibeller, B., Luiz, A. J. B., Soares, M. D., Prudente, V. H. R., Vieira, D. C., and Maurano, L. E. P.: LEM Benchmark database for tropical agricultural remote sensing application., Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 387-392, https://doi.org/10.5194/isprs-archives-XLII-1-387-2018, 2018.
Sanches, I. D.; Feitosa, R. Q.; Achanccaray, P.; Soares, M. D.; Luiz, A.; Schultz, B.; Maurano, L. Campo Verde Database: Seeking to improve agricultural remote sensing of tropical areas. IEEE Geoscience and Remote Sensing Letters, 15(3):369-373, 2018.
Cué, L. E.; Bermudez, J. D.; Achanccaray, P.; Sanches, I. D.; Happ, P. N.; Feitosa, R. Q. A comparative analysis of deep learning techniques for crop type recognition in temperate and tropical regions from multitemporal SAR image sequences. Anais do XXVII Congresso Brasileiro de Cartografia e XXVI Exposicarta 6 a 9 de novembro de 2017, SBC, Rio de Janeiro – RJ, p. 730-734
Bermúdez, J. D.; Achanccaray, P.; Sanches, I. D.; Cue, L.; Happ, P.; Feitosa, R. Q. Evaluation of recurrent neural networks for crop recognition from multitemporal remote sensing images. Anais do XXVII Congresso Brasileiro de Cartografia e XXVI Exposicarta 6 a 9 de novembro de 2017, SBC, Rio de Janeiro – RJ, p. 800-804
MUSCI, M.; HAPP, P. N.; COSTA, G. A. O. P.; FEITOSA, R. Q. Utilizando um cluster virtual com Hadoop como uma ferramenta para exploração de big data em processamento de imagens digitais In: XVIII Simpósio Brasileiro de Sensoriamento Remoto, 2017, Santos. Anais do XVIII Simpósio Brasileiro de Sensoriamento Remoto. São José dos Campos: INPE, 2017. p.7489 – 7495
HAPP, P. N.; COSTA, G. A. O. P.; FEITOSA, R. Q. Uma comparação entre MapReduce e Tez para segmentação de imagens em ambientes de computação em nuvem In: XVIII Simpósio Brasileiro de Sensoriamento Remoto, 2017, Santos. Anais do XVIII Simpósio Brasileiro de Sensoriamento Remoto. São Jose dos Campos: INPE, 2017. p.7938 – 7945
ACHANCCARAY DIAZ, PEDRO; FEITOSA, R. Q.; ROTTENSTEINER, F.; SANCHES, I. D. A.; HEIPKE, CHRISTIAN. Spatio-temporal Conditional Random Fields for recognition of sub-tropical crop types from multi-temporal images In: XVIII Simpósio Brasileiro de Sensoriamento Remoto, 2017, Santos. Anais do XVIII Simpósio Brasileiro de Sensoriamento Remoto. São José dos Campos: INPE, 2017. p.2539 – 2546
DIAZ, P. M. A.; FEITOSA, R. Q.; ROTTENSTEINER, F.; SANCHES, I. D. A.; HEIPKE, C. Spatial-temporal conditional random field based model for crop recognition in tropical regions In: 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017, Fort Worth. IGARSS 2017 Proceedings. IEEE, 2017. p.3007 – 3010