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
Publications
MAPPING EUCALYPTUS PLANTATIONS AND NATURAL FOREST AREAS IN LANDSAT-TM IMAGES USING DEEP LEARNING
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.
A Comparison Of Fully Convolutional And Recurrent Networks For Multi-Temporal Crop Recognition Using SAR Images
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.
Assessment of an Early Fusion CNN Approach applied to the deforestation detection in the Brazilian Amazon
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.
Unsupervised Representation Learning for Classification of Remote Sensing images based on Generative Adversarial Networks
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).
Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification
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
An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences
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.
Dense Fully Convolutional Networks for Crop Recognition from Multitemporal SAR Image Sequences
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.
Coping with environmental challenges in Latin America
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.
SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS
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.
LEM Benchmark database for tropical agricultural remote sensing application
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.
Campo Verde Database: Seeking to improve agricultural remote sensing of tropical areas
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.
A comparative analysis of deep learning techniques for crop type recognition in temperate and tropical regions from multitemporal SAR image sequences
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
Evaluation of recurrent neural networks for crop recognition from multitemporal remote sensing images
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
Utilizando um cluster virtual com Hadoop como uma ferramenta para exploração de big data em processamento de imagens digitais
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
Uma comparação entre MapReduce e Tez para segmentação de imagens em ambientes de computação em nuvem
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
Spatio-temporal Conditional Random Fields for recognition of sub-tropical crop types from multi-temporal images
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
Spatial-temporal conditional random field based model for crop recognition in tropical regions
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
Exploiting Different Types of Parallelism in Distributed Analysis of Remote Sensing Data
COSTA, GILSON A. O. P.; BENTES, CRISTIANA; FERREIRA, RODRIGO S.; FEITOSA, RAUL Q.; OLIVEIRA, DARIO A. B. Exploiting Different Types of Parallelism in Distributed Analysis of Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters. , v.14, p.1 – 5, 2017.
A Comparative Analysis of Deep Learning Techniques for Sub-Tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences
BERMUDEZ, J. ; FEITOSA, RAUL Q. ; CUE, L. ; ACHANCCARAY, P. ; SANCHES, I. D. A Comparative Analysis of Deep Learning Techniques for Sub-Tropical Crop Types Recognition from Multitemporal Optical/SAR Image Sequences. In: 2017 30th Conference on Graphics, Patterns and Images (SIBGRAPI), Niteroi, 2017. p. 382-389.