Deep Learning and Computer Vision in Remote Sensing-II

• Andere auteurs: Farahnakian, Fahimeh (Redacteur), Heikkonen, Jukka (Redacteur), Jafarzadeh, Pouya (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Onderwerpen: Information technology industries; Computer science; pose estimation; landmark regression; space target; 1D landmark representation; deep learning; convolutional neural network (CNN); deep supervision; lightweight model; remote sensing; semantic segmentation; convolutional neural networks (CNNs); remote sensing images; object detection; knowledge inference module; convolutional neural networks; tree ensemble methods; multi-label classification; complex-valued U-Net; complex-valued capsule network; polarimetric synthetic aperture radar; unmanned aerial vehicle (UAV); grassland grazing livestock; remote sensing image; artificial intelligence; building extraction; multi-scale object detection; multi-feature fusion and attention network; multi-branch convolution; attention mechanism; loss function; remote-sensing image; neural architecture search; sparse regularization; HRNet; Earth observation; land use and land cover classification; transfer learning; dynamic resolution adaptation; small-object detection; machine learning; data augmentation; automatic target recognition; synthetic aperture radar; spacecraft recognition; few-shot feature adaptation; generative family; neural processes; remote sensing imagery; transformer; Landsat
• Online toegang: DOAB: download the publication; DOAB: description of the publication

Computer Vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive r...