
|
03 Aug 2020
SEMANTIC SEGMENTATION OF BRAZILIAN SAVANNA VEGETATION USING HIGH SPATIAL RESOLUTION SATELLITE DATA AND U-NET
A. K. Neves, T. S. Körting, L. M. G. Fonseca, C. D. Girolamo Neto, D. Wittich, G. A. O. P. Costa, and C. Heipke
Related authors
IRRIGATED AGRICULTURE MAPPING IN A SEMI-ARID REGION IN BRAZIL BASED ON THE USE OF SENTINEL-2 DATA AND RANDOM FOREST ALGORITHM
H. N. Bendini, L. M. G. Fonseca, C. A. Bertolini, R. F. Mariano, A. S. Fernandes Filho, T. H. Fontenelle, and D. A. C. Ferreira
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 33–39, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-33-2023,https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-33-2023, 2023
A DEBIASING VARIATIONAL AUTOENCODER FOR DEFORESTATION MAPPING
M. X. Ortega Adarme, P. J. Soto Vega, G. A. O. P. Costa, R. Q. Feitosa, and C. Heipke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 217–223, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-217-2023,https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-217-2023, 2023
IMAGE-BASED DEEP LEARNING FOR RHEOLOGY DETERMINATION OF BINGHAM FLUIDS
A. Ponick, A. Langer, D. Beyer, M. Coenen, M. Haist, and C. Heipke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 711–720, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-711-2022,https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-711-2022, 2022
A COMPARISON OF CLOUD REMOVAL METHODS FOR DEFORESTATION MONITORING IN AMAZON RAINFOREST
J. A. C. Martinez, M. X. O. Adarme, J. N. Turnes, G. A. O. P. Costa, C. A. De Almeida, and R. Q. Feitosa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 665–671, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-665-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-665-2022, 2022
EVALUATING THE SEPARABILITY BETWEEN DRY TROPICAL FORESTS AND SAVANNA WOODLANDS IN THE BRAZILIAN SAVANNA USING LANDSAT DENSE IMAGE TIME SERIES AND ARTIFICIAL INTELLIGENCE
H. N. Bendini, L. M. G. Fonseca, B. M. Matosak, R. F. Mariano, R. F. Haidar, and D. M. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 841–847, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-841-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-841-2022, 2022
COOPERATIVE IMAGE ORIENTATION CONSIDERING DYNAMIC OBJECTS
P. Trusheim, M. Mehltretter, F. Rottensteiner, and C. Heipke
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 169–177, https://doi.org/10.5194/isprs-annals-V-1-2022-169-2022,https://doi.org/10.5194/isprs-annals-V-1-2022-169-2022, 2022
ADVERSARIAL DISCRIMINATIVE DOMAIN ADAPTATION FOR DEFORESTATION DETECTION
J. Noa, P. J. Soto, G. A. O. P. Costa, D. Wittich, R. Q. Feitosa, and F. Rottensteiner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 151–158, https://doi.org/10.5194/isprs-annals-V-3-2021-151-2021,https://doi.org/10.5194/isprs-annals-V-3-2021-151-2021, 2021
DEFORESTATION MONITORING IN DIFFERENT BRAZILIAN BIOMES: CHALLENGES AND LESSONS
C. A. Almeida, D. M. Valeriano, L. Maurano, L. Vinhas, L. M. G. Fonseca, D. Silva, C. P. F. Santos, F. S. R. V. Martins, F. C. B. Lara, J. S. Maia, E. R. Profeta, L. O. Santos, F. C. O. Santos, and V. Ribeiro
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 47–52, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-47-2020,https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-47-2020, 2020
EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON
R. B. Andrade, G. A. O. P. Costa, G. L. A. Mota, M. X. Ortega, R. Q. Feitosa, P. J. Soto, and C. Heipke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1497–1505, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020, 2020
DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST
P. J. Soto, G. A. O. P. Costa, R. Q. Feitosa, P. N. Happ, M. X. Ortega, J. Noa, C. A. Almeida, and C. Heipke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1635–1643, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1635-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1635-2020, 2020
COMBINING ENVIRONMENTAL AND LANDSAT ANALYSIS READY DATA FOR VEGETATION MAPPING: A CASE STUDY IN THE BRAZILIAN SAVANNA BIOME
H. N. Bendini, L. M. G. Fonseca, M. Schwieder, P. Rufin, T. S. Korting, A. Koumrouyan, and P. Hostert
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 953–960, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-953-2020,https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-953-2020, 2020
A COMPARISON BETWEEN THE HADOOP AND SPARK DISTRIBUTED FRAMEWORKS IN THE CONTEXT OF REGION-GROWING SEGMENTATION OF REMOTE SENSING IMAGES
R. B. Andrade, J. M. F. Santos, G. A. O. P. Costa, G. L. A. Mota, P. N. Happ, and R. Q. Feitosa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 3–8, https://doi.org/10.5194/isprs-annals-IV-2-W7-3-2019,https://doi.org/10.5194/isprs-annals-IV-2-W7-3-2019, 2019
Editorial Note
C. Heipke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5-W7, 543–543, https://doi.org/10.5194/isprs-archives-XL-5-W7-543-2016,https://doi.org/10.5194/isprs-archives-XL-5-W7-543-2016, 2016