Road detection in SAR / PolSAR image

dc.contributor.authorDe Borba A.A.
dc.contributor.authorMarengoni M.
dc.contributor.authorFrery A.C.
dc.date.accessioned2024-07-01T06:11:32Z
dc.date.available2024-07-01T06:11:32Z
dc.date.issued2024
dc.description.abstract© 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.
dc.identifier.doi10.1109/MIGARS61408.2024.10544995
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/38808
dc.relation.ispartof2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024
dc.rightsAcesso Restrito
dc.subject.otherlanguageConvolutional Neural Network
dc.subject.otherlanguageNoise speckle
dc.subject.otherlanguagePolSAR image
dc.subject.otherlanguageSAR image
dc.subject.otherlanguageU-NET
dc.titleRoad detection in SAR / PolSAR image
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85196110262
local.scopus.subjectCapture images
local.scopus.subjectConvolutional neural network
local.scopus.subjectNoise speckle
local.scopus.subjectOptical image
local.scopus.subjectPolarimetric synthetic aperture radar image
local.scopus.subjectPolarimetric synthetic aperture radars
local.scopus.subjectRoad detection
local.scopus.subjectSynthetic aperture radar images
local.scopus.subjectU-NET
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85196110262&origin=inward
Arquivos