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Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
| Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images |
| Autore | Bazi Yakoub |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (438 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
synthetic aperture radar
despeckling multi-scale LSTM sub-pixel high-resolution remote sensing imagery road extraction machine learning DenseUNet scene classification lifting scheme convolution CNN image classification deep features hand-crafted features Sinkhorn loss remote sensing text image matching triplet networks EfficientNets LSTM network convolutional neural network water identification water index semantic segmentation high-resolution remote sensing image pixel-wise classification result correction conditional random field (CRF) satellite object detection neural networks single-shot deep learning global convolution network feature fusion depthwise atrous convolution high-resolution representations ISPRS vaihingen Landsat-8 faster region-based convolutional neural network (FRCNN) single-shot multibox detector (SSD) super-resolution remote sensing imagery edge enhancement satellites open-set domain adaptation adversarial learning min-max entropy pareto ranking SAR Sentinel–1 Open Street Map U–Net desert road infrastructure mapping monitoring deep convolutional networks outline extraction misalignments nearest feature selector hyperspectral image classification two stream residual network Batch Normalization plant disease detection precision agriculture UAV multispectral images orthophotos registration 3D information orthophotos segmentation wildfire detection convolutional neural networks densenet generative adversarial networks CycleGAN data augmentation pavement markings visibility framework urban forests OUDN algorithm object-based high spatial resolution remote sensing Generative Adversarial Networks post-disaster building damage assessment anomaly detection Unmanned Aerial Vehicles (UAV) xBD feature engineering orthophoto unsupervised segmentation |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557747903321 |
Bazi Yakoub
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Generative Adversarial Networks for Image Generation / Xudong Mao, Qing Li
| Generative Adversarial Networks for Image Generation / Xudong Mao, Qing Li |
| Autore | Mao, Xudong |
| Pubbl/distr/stampa | Singapore, : Springer, 2021 |
| Descrizione fisica | xii, 77 p. : ill. ; 24 cm |
| Altri autori (Persone) | Li, Qing |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T07 - Artificial neural networks and deep learning [MSC 2020] |
| Soggetto non controllato |
Adversarial Networks
Deep Learning Generative Adversarial Networks Generative models Image Generation Image to Image Translation Machine learning Neural networks Unsupervised Domain Adaptation |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0275456 |
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Mao, Xudong
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| Singapore, : Springer, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Machine Learning Applications in Electronic Design Automation / Haoxing Ren, Jiang Hu editors
| Machine Learning Applications in Electronic Design Automation / Haoxing Ren, Jiang Hu editors |
| Pubbl/distr/stampa | Cham, : Springer, 2022 |
| Descrizione fisica | xii, 583 p. : ill. ; 24 cm |
| Soggetto non controllato |
Convolutional Neural Networks
Deep learning for EDA Generative Adversarial Networks Graph neural networks Reinforcement Learning |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN0277814 |
| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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