Deep Learning and Computer Vision in Remote Sensing

• Diğer Yazarlar: Farahnakian, Fahimeh (Editör), Heikkonen, Jukka (Editör), Jafarzadeh, Pouya (Editör)
• Materyal Türü: Elektronik Kitap Bölümü
• Dil: İngilizce
• Baskı/Yayın Bilgisi: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Konular: Technology: general issues; History of engineering & technology; tropical cyclone detection; meteorological satellite images; deep learning; deep transfer learning; generative adversarial networks; image target detection; multiple scales; any angle object; remote sensing of small objects; point clouds; 3D tracking; state estimation; Siamese network; deep LK; convolutional neural networks (CNNs); multilayer feature aggregation; attention mechanism; remote sensing image scene classification (RSISC); hyperspectral image classification; variational autoencoder; generative adversarial network; crossed spatial and spectral interactions; crater detection algorithm (CDA); R-FCN; self-calibrated convolution; split attention mechanism; transfer learning; remote sensing; oriented object detection; rotated inscribed ellipse; remote sensing images; keypoint-based detection; gated aggregation; eccentricity-wise; object detection; remote sensing image; anchor free; oriented bounding boxes; deformable convolution; three-dimensional radar imaging; convolution neural network; super-resolution; side-lobe suppression; terahertz radar; aerial image generation; satellite image generation; structure map; style vector; high resolution image; self-constructing graph; semantic segmentation; GAN; image generation; data augmentation; remote sensing disaster image; convolutional neural network; double-stream structure; feedback; encoder-decoder network; dense connections; instance segmentation; Swin transformer; cascade mask R-CNN; remote sensing image retrieval; hashing algorithm; binary code; triplet ordinal relation preserving; cross entropy; feature distillation; forest fire; smoke segmentation; Smoke-Unet; residual block; Landsat-8; band sensibility; unsupervised domain adaptation; bidirectional domain adaptation; image-to-image translation; generative adversarial networks (GANs); U-Net; high-density laser scanning; logging trails; digital surface model; canopy height model; commercial thinning; convolutional neural networks; multiview; satellite and UAV image; joint description; image matching; neural network; contextual information; Anchor Free Region Proposal Network; polar representation; 3D object detection; point cloud; sampling; single-stage; rotated object detection; angle-based detector; angle-free framework; rotated region of interests (RRoIs); representative points; plastic; UAVs; contrastive learning; mutual guidance; spatial misalignment; vehicle detection; ANN; automatic classification; risk mitigation; machine learning
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