Artificial Intelligence-Based Learning Approaches for Remote Sensing
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...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (382 p.) |
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Jeon, Gwanggil edt Artificial Intelligence-Based Learning Approaches for Remote Sensing Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (382 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Open access Unrestricted online access star 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. English Technology: general issues bicssc History of engineering & technology bicssc Environmental science, engineering & technology bicssc 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 3-0365-6083-1 Jeon, Gwanggil oth |
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English |
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| author2 |
Jeon, Gwanggil |
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Jeon, Gwanggil |
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| title |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| spellingShingle |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| title_full |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| title_fullStr |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
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Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| title_auth |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| title_new |
Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| title_sort |
artificial intelligence-based learning approaches for remote sensing |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (382 p.) |
| isbn |
3-0365-6084-X 3-0365-6083-1 |
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Not Illustrated |
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AT jeongwanggil artificialintelligencebasedlearningapproachesforremotesensing |
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Artificial Intelligence-Based Learning Approaches for Remote Sensing |
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