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Proposta de Sistema de Monitoramento da Sigatoka-Negra Baseado em Variáveis Ambientais Utilizando o TerraMA2.

, , , , , and . GeoInfo, page 168-173. MCTI/INPE, (2014)

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Exploring a Deep Convolutional Neural Network and Geobia for Automatic Recognition of Brazilian Palm Swamps (Veredas) Using Sentinel-2 Optical Data., , , , , , , and . IGARSS, page 5401-5404. IEEE, (2021)Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest., , , , , and . IEEE Geosci. Remote. Sens. Lett., 18 (5): 771-775 (2021)Combining Time Series Features and Data Mining to Detect Land Cover patterns: a Case Study in Northern Mato Grosso State, Brazil., , , and . GEOINFO, page 174-185. MCTI/INPE, (2015)Assessment of a multi-sensor approach for noise removal on Landsat-8 OLI time series using CBERS-4 MUX data to improve crop classification based on phenological features., , , , , and . GEOINFO, page 240-251. MCTIC/INPE, (2016)Proposta de Sistema de Monitoramento da Sigatoka-Negra Baseado em Variáveis Ambientais Utilizando o TerraMA2., , , , , and . GeoInfo, page 168-173. MCTI/INPE, (2014)Segmentation of optical remote sensing images for detecting homogeneous regions in space and time., , , , , , , , and . GEOINFO, page 40-51. MCTIC/INPE, (2017)Applying A Phenological Object-Based Image Analysis (Phenobia) for Agricultural Land Classification: A Study Case in the Brazilian Cerrado., , , , , , , , , and 1 other author(s). IGARSS, page 1078-1081. IEEE, (2020)Detecting Clearcut Deforestation Employing Deep Learning Methods and SAR Time Series., , , , , , and . IGARSS, page 4520-4523. IEEE, (2021)Mapping Flooded Rice in Brazil., , and . GEOINFO, page 376-381. MCTI/INPE, (2023)Stmetrics: A Python Package for Satellite Image Time-Series Feature Extraction., , , , , , , and . IGARSS, page 2061-2064. IEEE, (2020)