Deep Learning and Computer Vision in Remote Sensing

• Autres auteurs: Farahnakian, Fahimeh (Éditeur intellectuel), Heikkonen, Jukka (Éditeur intellectuel), Jafarzadeh, Pouya (Éditeur intellectuel)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Sujets: 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
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Résumé

In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.

Description matérielle

1 electronic resource (572 p.)

ISBN

books978-3-0365-6369-5
9783036563688
9783036563695

Accès

Open Access