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A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery.

, , , , , и . Remote. Sens., 12 (6): 961 (2020)

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Rogério Mugnaini

Rogério Craveiro

Rogério Richa

Rogério Rossi

Rogério Chaves Vieira

 

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Semi-supervised remote sensing image classification methods assessment., , и . IGARSS, стр. 2939-2942. IEEE, (2011)Context Adaptive Support Vector Machine: formalization and Remote Sensing Applications.. National Institute for Space Research, São José dos Campos, Brazil, (2013)ndltd.org (oai:agregador.ibict.br.BDTD_INPE:oai:urlib.net:sid.inpe.br/mtc-m19/2013/07.10.14.32.48-0).Region-based classification of PolSAR data using radial basis kernel functions with stochastic distances., , , , и . Int. J. Digit. Earth, 12 (6): 699-719 (2019)Description of land cover and susceptibility to fire in forest areas using spatial metrics., , , и . GEOINFO, стр. 66-74. MCTIC/INPE, (2021)Aplicação de Modelos de Aprendizado Semissupervisionado na Classificação de Imagens de Sensoriamento Remoto., , и . RITA, 20 (2): 32-55 (2013)Spectral-Spatial-Aware Unsupervised Change Detection With Stochastic Distances and Support Vector Machines., , , , , , и . IEEE Trans. Geosci. Remote. Sens., 59 (4): 2863-2876 (2021)Robust Seeded Image Segmentation Using Adaptive Label Propagation and Deep Learning-Based Contour Orientation., , , и . ICCSA (2), том 13957 из Lecture Notes in Computer Science, стр. 19-31. Springer, (2023)Mapeamento da susceptibilidade à incêndios através de Sensoriamento Remoto no município de Cáceres., , , и . GEOINFO, стр. 270-274. MCTIC/INPE, (2022)Remote sensing and machine learning on anomaly detection at high spectral and temporal dynamics regions in Brazil., , и . GEOINFO, стр. 99-110. MCTIC/INPE, (2022)Stochastic Approaches of Minimum Distance Method for Region Based Classification., , и . CIARP, том 7441 из Lecture Notes in Computer Science, стр. 797-804. Springer, (2012)