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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 online resource (438 p.) |
| Soggetto topico | Research and information: general |
| Soggetto non controllato |
3D information
adversarial learning anomaly detection Batch Normalization building damage assessment CNN conditional random field (CRF) convolution convolutional neural network convolutional neural networks CycleGAN data augmentation deep convolutional networks deep features deep learning densenet DenseUNet depthwise atrous convolution desert despeckling edge enhancement EfficientNets faster region-based convolutional neural network (FRCNN) feature engineering feature fusion framework generative adversarial networks Generative Adversarial Networks global convolution network hand-crafted features high spatial resolution remote sensing high-resolution remote sensing image high-resolution remote sensing imagery high-resolution representations hyperspectral image classification image classification infrastructure ISPRS vaihingen Landsat-8 lifting scheme LSTM LSTM network machine learning mapping min-max entropy misalignments monitoring multi-scale nearest feature selector neural networks object detection object-based Open Street Map open-set domain adaptation orthophoto orthophotos registration orthophotos segmentation OUDN algorithm outline extraction pareto ranking pavement markings pixel-wise classification plant disease detection post-disaster precision agriculture remote sensing remote sensing imagery result correction road road extraction SAR satellite satellites scene classification semantic segmentation Sentinel-1 single-shot single-shot multibox detector (SSD) Sinkhorn loss sub-pixel super-resolution synthetic aperture radar text image matching triplet networks two stream residual network U-Net UAV multispectral images Unmanned Aerial Vehicles (UAV) unsupervised segmentation urban forests visibility water identification water index wildfire detection xBD |
| 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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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-VAN00275456 |
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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 topico |
94-XX - Information and communication theory, circuits [MSC 2020]
94Cxx - Circuits, networks [MSC 2020] |
| 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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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 topico |
94-XX - Information and communication theory, circuits [MSC 2020]
94Cxx - Circuits, networks [MSC 2020] |
| 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-VAN00277814 |
| Cham, : Springer, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Machine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook / Lior Rokach, Oded Maimon, Erez Shmueli editors
| Machine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook / Lior Rokach, Oded Maimon, Erez Shmueli editors |
| Edizione | [3. ed] |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | vii, 985 p. : ill. ; 24 cm |
| Soggetto topico |
68-XX - Computer science [MSC 2020]
68T05 - Learning and adaptive systems in artificial intelligence [MSC 2020] 68T09 - Computational aspects of data analysis and big data [MSC 2020] |
| Soggetto non controllato |
Analytics
Artificial Intelligence Autoencoder Clustering Data Mining Data imputation Data science Decision Trees Deep Learning Dimensionality reduction Ensemble Learning Generative Adversarial Networks Knowledge Discovery Machine learning Neural networks Privacy Preserving Recommender system Text Mining Web mining |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278867 |
| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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