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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.

Titel:	Artificial Intelligence-Based Learning Approaches for Remote Sensing
Autor/in / Beteiligte Person:	Jeon, Gwanggil
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	Basel: MDPI - Multidisciplinary Digital Publishing Institute, 2022
Medientyp:	E-Book
Umfang:	382
ISBN:	978-3-0365-6083-0 (print) ; 978-3-0365-6084-7 (print)
DOI:	10.3390/books978-3-0365-6084-7
Schlagwort:	pine wilt disease dataset GIS application visualization test-time augmentation object detection hard negative mining video synthetic aperture radar (SAR) moving target shadow detection deep learning false alarms missed detections synthetic aperture radar (SAR) on-board ship detection YOLOv5 lightweight detector remote sensing image spectral domain translation generative adversarial network paired translation synthetic aperture radar ship instance segmentation global context modeling boundary-aware box prediction land-use and land-cover built-up expansion probability modelling landscape fragmentation machine learning support vector machine frequency ratio fuzzy logic artificial intelligence remote sensing interferometric phase filtering sparse regularization (SR) deep learning (DL) neural convolutional network (CNN) semantic segmentation open data building extraction unet deeplab classifying-inversion method AIS atmospheric duct ship detection and classification rotated bounding box attention feature alignment weather nowcasting ResNeXt radar data spectral-spatial interaction network spectral-spatial attention pansharpening UAV visual navigation Siamese network multi-order feature MIoU imbalanced data classification data over-sampling graph convolutional network semi-supervised learning troposcatter tropospheric turbulence intercity co-channel interference concrete bridge visual inspection defect deep convolutional neural network transfer learning interpretation techniques weakly supervised semantic segmentation 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 thema EDItEUR:T Technology, Engineering, Agriculture, Industrial processes:TQ Environmental science, engineering and technology
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: image/jpeg Language: English Rights: Attribution 4.0 International ; URL: https://creativecommons.org/licenses/by/4.0/ Notes: ONIX_20230105_9783036560830_47

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Artificial Intelligence-Based Learning Approaches for Remote Sensing