Road detection in SAR / PolSAR image
Tipo
Artigo de evento
Data de publicação
2024
Periódico
2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024
Citações (Scopus)
0
Autores
De Borba A.A.
Marengoni M.
Frery A.C.
Marengoni M.
Frery A.C.
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Título de Volume
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Resumo
© 2024 IEEE.Optical sensors are not able to capture images under the effect of severe weather conditions and poor lighting, making the task of extracting information from optical images difficult. The application of convolutional neural networks (CNNs) to Synthetic Aperture Radar (SAR)/ Polarimetric Synthetic Aperture Radar (PolSAR) images can mitigate these conceptual limitations characteristic of optical images due to the electromagnetic nature of SAR / PolSAR data capture sensors.The problem we propose in the research is finding a suitable configuration for the CNN to detect roads in SAR / PolSAR images with accuracy comparable to or better than the state-of-the-art and with excellent computational efficiency.
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Assuntos Scopus
Capture images , Convolutional neural network , Noise speckle , Optical image , Polarimetric synthetic aperture radar image , Polarimetric synthetic aperture radars , Road detection , Synthetic aperture radar images , U-NET