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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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Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019
| Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019 |
| Autore | Matese Alessandro |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (184 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Forestry & related industries |
| Soggetto non controllato |
unmanned aerial vehicles
seedling detection forest regeneration reforestation establishment survey machine learning multispectral classification UAV photogrammetry forest modeling ancient trees measurement tree age prediction Mauritia flexuosa semantic segmentation end-to-end learning convolutional neural network forest inventory Unmanned Aerial Systems (UAS) structure from motion (SfM) Unmanned Aerial Vehicles (UAV) Photogrammetry Thematic Mapping Accuracy Assessment Reference Data Forest Sampling Remote Sensing Robinia pseudoacacia L. reproduction spreading short rotation coppice unmanned aerial system (UAS) object-based image analysis (OBIA) convolutional neural network (CNN) juniper woodlands ecohydrology remote sensing unmanned aerial systems central Oregon rangelands seedling stand inventorying photogrammetric point clouds hyperspectral imagery leaf-off leaf-on UAV multispectral image forest fire burn severity classification precision agriculture biomass evaluation image processing Castanea sativa unmanned aerial vehicles (UAV) precision forestry forestry applications RGB imagery |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Forestry Applications of Unmanned Aerial Vehicles |
| Record Nr. | UNINA-9910557112103321 |
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Matese Alessandro
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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