Deep Learning and Computer Vision in Remote Sensing

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 al...

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Dettagli Bibliografici
Natura:	Online
Lingua:	inglese
Pubblicazione:	MDPI - Multidisciplinary Digital Publishing Institute 2023
Soggetti:	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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Accesso online:	ONIX_20230307_9783036563688_60
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Riassunto:	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.

Deep Learning and Computer Vision in Remote Sensing

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