@inproceedings{Stabile2025AutomaticDetection,

title = {Automatic Detection of Airstrips in the Amazon Using ICEYE SAR Imagery and U-Net Neural Networks},

author = {Gustavo Henrique de Queiroz Stabile and Dimas Irion Alves and Tahisa Neitzel Kuck and Paulo Ricardo Branco Silva and Saleh Javadi},

url = {https://www.sige.ita.br/edicoes-anteriores/2025/TRABALHOS/Artigos-com-ISSN/Automatic Detection of Airstrips in the Amazon Using ICEYE SAR Imagery and U-Net Neural Networks.pdf},

issn = {1983-7402},

year  = {2025},

date = {2025-01-01},

booktitle = {Anais do Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa (SIGE)},

pages = {164\textendash169},

address = {S\~{a}o Jos\'{e} dos Campos, Brasil},

organization = {Instituto Tecnol\'{o}gico de Aeron\'{a}utica},

abstract = {This study investigates convolutional neural networks, specifically the U-Net architecture, for the automated detection of irregular airstrips in the Amazon rainforest using Synthetic Aperture Radar (SAR) imagery from ICEYE satellites operating in the X-band. Detecting these airstrips is strategic for combating illicit activities and protecting the environment. SAR imagery is particularly effective in the Amazon, as it can penetrate cloud coverage, which is common in the region. A comprehensive pipeline was developed for data preparation, model training, and evaluation, utilizing the airstrips cataloged by the MapBiomas project for reference. Experiments were conducted by varying both the size of the input images and the balance of the training dataset. Results indicated that even with a limited number of images, the U-Net architecture can generate consistent outcomes. The study supports the development of operational solutions for the automated monitoring of irregular airstrips in the Amazon region.},

keywords = {airstrips, automatic detection, Deep Learning, Synthetic Aperture Radar},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Leonan2019Syntheticb,

title = {kNN e Rede Neural Convolucional para o Reconhecimento de Plataformas de Petr\'{o}leo em Imagens SAR do Sentinel-1},

author = {Leonan Entringer Falqueto and Ricardo Suterio and Rafael Lemos Paes and Angelo Passaro},

url = {https://spectrum.ita.br/index.php/spectrum/article/view/395},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa 2019 (SIGE2019)},

abstract = {O reconhecimento autom\'{a}tico de alvos (plataformas petrol\'{i}feras) por meio de imagens de radar de abertura sint\'{e}tica (SAR) de m\'{e}dia resolu\c{c}\~{a}o auxilia a vigil\^{a}ncia de \'{a}reas extensas como o Atl\^{a}ntico Sul. Esse trabalho aprofundou o estudo do emprego da VGG-16 como extratora de atributos para alimentar algoritmos de Machine Learning, especificamente, o kNN. Variou-se o n\'{u}mero de vizinhos para um conjunto de amostras de imagens SAR do Sentinel-1 contendo plataformas mar\'{i}timas e falsos-alarmes, usando um experimento com 50 blocos de treinamento e teste. Demonstrou- se que o ajuste de par\^{a}metros do classificador apresenta melhorias significativas, com um incremento de 6,46% no indicador AUC.},

keywords = {Synthetic Aperture Radar},

pubstate = {published},

tppubtype = {inproceedings}

}