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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| Sonstige: | |
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (382 p.) |
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| 100 | 1 | |a Jeon, Gwanggil |4 edt | |
| 245 | 1 | 0 | |a Artificial Intelligence-Based Learning Approaches for Remote Sensing |
| 260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
| 300 | |a 1 electronic resource (382 p.) | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 650 | 7 | |a Environmental science, engineering & technology |2 bicssc | |
| 653 | |a pine wilt disease dataset | ||
| 653 | |a GIS application visualization | ||
| 653 | |a test-time augmentation | ||
| 653 | |a object detection | ||
| 653 | |a hard negative mining | ||
| 653 | |a video synthetic aperture radar (SAR) | ||
| 653 | |a moving target | ||
| 653 | |a shadow detection | ||
| 653 | |a deep learning | ||
| 653 | |a false alarms | ||
| 653 | |a missed detections | ||
| 653 | |a synthetic aperture radar (SAR) | ||
| 653 | |a on-board | ||
| 653 | |a ship detection | ||
| 653 | |a YOLOv5 | ||
| 653 | |a lightweight detector | ||
| 653 | |a remote sensing image | ||
| 653 | |a spectral domain translation | ||
| 653 | |a generative adversarial network | ||
| 653 | |a paired translation | ||
| 653 | |a synthetic aperture radar | ||
| 653 | |a ship instance segmentation | ||
| 653 | |a global context modeling | ||
| 653 | |a boundary-aware box prediction | ||
| 653 | |a land-use and land-cover | ||
| 653 | |a built-up expansion | ||
| 653 | |a probability modelling | ||
| 653 | |a landscape fragmentation | ||
| 653 | |a machine learning | ||
| 653 | |a support vector machine | ||
| 653 | |a frequency ratio | ||
| 653 | |a fuzzy logic | ||
| 653 | |a artificial intelligence | ||
| 653 | |a remote sensing | ||
| 653 | |a interferometric phase filtering | ||
| 653 | |a sparse regularization (SR) | ||
| 653 | |a deep learning (DL) | ||
| 653 | |a neural convolutional network (CNN) | ||
| 653 | |a semantic segmentation | ||
| 653 | |a open data | ||
| 653 | |a building extraction | ||
| 653 | |a unet | ||
| 653 | |a deeplab | ||
| 653 | |a classifying-inversion method | ||
| 653 | |a AIS | ||
| 653 | |a atmospheric duct | ||
| 653 | |a ship detection and classification | ||
| 653 | |a rotated bounding box | ||
| 653 | |a attention | ||
| 653 | |a feature alignment | ||
| 653 | |a weather nowcasting | ||
| 653 | |a ResNeXt | ||
| 653 | |a radar data | ||
| 653 | |a spectral-spatial interaction network | ||
| 653 | |a spectral-spatial attention | ||
| 653 | |a pansharpening | ||
| 653 | |a UAV visual navigation | ||
| 653 | |a Siamese network | ||
| 653 | |a multi-order feature | ||
| 653 | |a MIoU | ||
| 653 | |a imbalanced data classification | ||
| 653 | |a data over-sampling | ||
| 653 | |a graph convolutional network | ||
| 653 | |a semi-supervised learning | ||
| 653 | |a troposcatter | ||
| 653 | |a tropospheric turbulence | ||
| 653 | |a intercity co-channel interference | ||
| 653 | |a concrete bridge | ||
| 653 | |a visual inspection | ||
| 653 | |a defect | ||
| 653 | |a deep convolutional neural network | ||
| 653 | |a transfer learning | ||
| 653 | |a interpretation techniques | ||
| 653 | |a weakly supervised semantic segmentation | ||
| 776 | |z 3-0365-6083-1 | ||
| 700 | 1 | |a Jeon, Gwanggil |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2024-03-02 08:12:37 Europe/Vienna |f system |c marc21 |a 2023-01-18 06:03:23 Europe/Vienna |g false | ||
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