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.
Publications
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.
Evaluation of Deep Learning techniques for deforestation detection in the Amazon Forest
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.
A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION
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.
Synthesis of Multispectral Optical Images From SAR/Optical Multitemporal Data Using Conditional Generative Adversarial Networks
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
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.