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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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Flood Forecasting Using Machine Learning Methods
| Flood Forecasting Using Machine Learning Methods |
| Autore | Chang Fi-John |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (376 p.) |
| Soggetto non controllato |
natural hazards &
artificial neural network flood routing the Three Gorges Dam backtracking search optimization algorithm (BSA) lag analysis artificial intelligence classification and regression trees (CART) decision tree real-time optimization ensemble empirical mode decomposition (EEMD) improved bat algorithm convolutional neural networks ANFIS method of tracking energy differences (MTED) adaptive neuro-fuzzy inference system (ANFIS) recurrent nonlinear autoregressive with exogenous inputs (RNARX) disasters flood prediction ANN-based models flood inundation map ensemble machine learning flood forecast sensitivity hydrologic models phase space reconstruction water level forecast data forward prediction early flood warning systems bees algorithm random forest uncertainty soft computing data science hydrometeorology LSTM rating curve method forecasting superpixel particle swarm optimization high-resolution remote-sensing images machine learning support vector machine Lower Yellow River extreme event management runoff series empirical wavelet transform Muskingum model hydrograph predictions bat algorithm data scarce basins Wilson flood self-organizing map big data extreme learning machine (ELM) hydroinformatics nonlinear Muskingum model invasive weed optimization rainfall–runoff flood forecasting artificial neural networks flash-flood streamflow predictions precipitation-runoff the upper Yangtze River survey parameters Haraz watershed ANN time series prediction postprocessing flood susceptibility modeling rainfall-runoff deep learning database LSTM network ensemble technique hybrid neural network self-organizing map (SOM) data assimilation particle filter algorithm monthly streamflow forecasting Dongting Lake machine learning methods micro-model stopping criteria Google Maps cultural algorithm wolf pack algorithm flood events urban water bodies Karahan flood St. Venant equations hybrid & hydrologic model |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346688303321 |
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Chang Fi-John
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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