Deep Learning and Computer Vision in Remote Sensing

doi:10.3390/books978-3-0365-6369-5

Reprint

Download Flyer

Deep Learning and Computer Vision in Remote Sensing

Edited by

February 2023

572 pages

ISBN978-3-0365-6368-8 (Hardback)
ISBN978-3-0365-6369-5 (PDF)

https://doi.org/10.3390/books978-3-0365-6369-5

Free download (PDF)

This book is a reprint of the Special Issue Deep Learning and Computer Vision in Remote Sensing that was published in

Engineering

Environmental & Earth Sciences

Summary

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.

Format

Hardback

License

Keywords

tropical cyclone detection; meteorological satellite images; deep learning; deep transfer learning; generative adversarial networks; image target detection; deep learning; 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; generative adversarial network; deep learning; structure map; style vector; high resolution image; self-constructing graph; semantic segmentation; remote sensing; GAN; image generation; data augmentation; remote sensing disaster image; convolutional neural network; double-stream structure; feedback; encoder–decoder network; dense connections; instance segmentation; object detection; Swin transformer; remote sensing image; cascade mask R-CNN; remote sensing image retrieval; hashing algorithm; binary code; triplet ordinal relation preserving; cross entropy; attention mechanism; feature distillation; remote sensing; super-resolution; forest fire; remote sensing; smoke segmentation; Smoke-Unet; attention mechanism; residual block; Landsat-8; band sensibility; unsupervised domain adaptation; bidirectional domain adaptation; convolutional neural networks (CNNs); image-to-image translation; generative adversarial networks (GANs); remote sensing images; semantic segmentation; U-Net; high-density laser scanning; logging trails; digital surface model; canopy height model; commercial thinning; semantic segmentation; convolutional neural networks; multiview; satellite and UAV image; joint description; image matching; neural network; remote sensing images; oriented object detection; 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; deep learning; transfer learning; plastic; UAVs; contrastive learning; mutual guidance; spatial misalignment; vehicle detection; ANN; automatic classification; risk mitigation; machine learning