Sildomar T. Monteiro
Sildomar T. Monteiro
Manager of AI Research, Aurora (Boeing)
Verified email at - Homepage
Cited by
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Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors
RJ Murphy, ST Monteiro, S Schneider
IEEE Transactions on Geoscience and Remote Sensing 50 (8), 3066-3080, 2012
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery
ST Monteiro, Y Minekawa, Y Kosugi, T Akazawa, K Oda
ISPRS Journal of Photogrammetry and Remote Sensing 62 (1), 2-12, 2007
Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm)
RJ Murphy, ST Monteiro
ISPRS Journal of Photogrammetry and Remote Sensing 75, 29-39, 2013
Dense semantic labeling of very-high-resolution aerial imagery and lidar with fully-convolutional neural networks and higher-order CRFs
Y Liu, S Piramanayagam, ST Monteiro, E Saber
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
Machine learning based hyperspectral image analysis: a survey
UB Gewali, ST Monteiro, E Saber
arXiv preprint arXiv:1802.08701, 2018
Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at 900 nm as an Indicator of Mineralogy
RJ Murphy, S Schneider, ST Monteiro
Geoscience and Remote Sensing, IEEE Transactions on 52 (5), 2843 - 2857, 2014
Rock recognition from MWD data: a comparative study of boosting, neural networks, and fuzzy logic
A Kadkhodaie-Ilkhchi, ST Monteiro, F Ramos, P Hatherly
IEEE Geoscience and Remote Sensing Letters 7 (4), 680-684, 2010
Transfer learning for high resolution aerial image classification
Y Liang, ST Monteiro, ES Saber
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 1-8, 2016
Mapping layers of clay in a vertical geological surface using hyperspectral imagery: Variability in parameters of SWIR absorption features under different conditions of …
RJ Murphy, S Schneider, ST Monteiro
Remote Sensing 6 (9), 9104-9129, 2014
3D geological modelling using laser and hyperspectral data
JI Nieto, ST Monteiro, D Viejo
2010 IEEE International Geoscience and Remote Sensing Symposium, 4568-4571, 2010
Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields
Y Liu, S Piramanayagam, ST Monteiro, E Saber
Journal of Applied Remote Sensing 13 (1), 016501, 2019
A particle swarm optimization-based approach for hyperspectral band selection
ST Monteiro, Y Kosugi
2007 IEEE Congress on Evolutionary Computation, 3335-3340, 2007
Robust stock value prediction using support vector machines with particle swarm optimization
TM Sands, D Tayal, ME Morris, ST Monteiro
2015 IEEE Congress on Evolutionary Computation (CEC), 3327-3331, 2015
Towards applying hyperspectral imagery as an intraoperative visual aid tool
ST Monteiro, Y Kosugi, K Uto, E Watanabe
Proc. 4th Int. Conf. on Visualization, Imaging and Image Processing, 483-488, 2004
Dual-channel densenet for hyperspectral image classification
G Yang, UB Gewali, E Ientilucci, M Gartley, ST Monteiro
IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018
Embedded feature selection of hyperspectral bands with boosted decision trees
ST Monteiro, RJ Murphy
2011 IEEE International Geoscience and Remote Sensing Symposium, 2361-2364, 2011
Particle swarms for feature extraction of hyperspectral data
ST Monteiro, Y Kosugi
IEICE transactions on information and systems 90 (7), 1038-1046, 2007
Desempenho de algoritmos de aprendizagem por reforço sob condições de ambiguidade sensorial em robótica móvel
ST Monteiro, CHC Ribeiro
Sba: Controle & Automação Sociedade Brasileira de Automatica 15, 320-338, 2004
On the development of a hyperspectral library for autonomous mining
S Schneider, R Murphy, ST Monteiro, EW Nettleton
Australasian Conference on Robotics and Automation, 2009
Gaussian processes for vegetation parameter estimation from hyperspectral data with limited ground truth
UB Gewali, ST Monteiro, E Saber
Remote Sensing 11 (13), 1614, 2019
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