Deep Learning and Computer Vision in Remote Sensing-II

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...

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Detaylı Bibliyografya
Materyal Türü:	Online
Dil:	İngilizce
Baskı/Yayın Bilgisi:	MDPI - Multidisciplinary Digital Publishing Institute 2023
Konular:	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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science
Online Erişim:	ONIX_20231130_9783036593647_241
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Diğer Bilgiler
Özet:	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 remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing. Here are some major topics that are addressed in this reprint: Satellite image processing and analysis based on deep learning; Deep learning for object detection, image classification, and semantic and instance segmentation; Deep learning for remote sensing scene understanding and classification; Transfer learning, deep reinforcement learning for remote sensing; Supervised and unsupervised representation learning for remote sensing environments.

Deep Learning and Computer Vision in Remote Sensing-II

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