Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II
This publication elucidates the application of advanced technologies, including machine learning and deep learning, rooted in artificial intelligence, to the realm of remote sensing. It delineates the methodology employed to address prevailing challenges associated with the processing of images and...
Furkejuvvon:
| Materiálatiipa: | Online |
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| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Fáttát: | |
| Liŋkkat: | ONIX_20240514_9783725807710_454 |
| Fáddágilkorat: |
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| _version_ | 1869515295978160128 |
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| collection | Directory of Open Access Books |
| description | This publication elucidates the application of advanced technologies, including machine learning and deep learning, rooted in artificial intelligence, to the realm of remote sensing. It delineates the methodology employed to address prevailing challenges associated with the processing of images and image signals in remote sensing contexts. These methodologies are inherently computation-intensive, necessitating the utilization of high-performance computing apparatus, notably GPUs. With the evolution of such computational devices, alongside advancements in remote and aerial sensing technologies, it has become feasible to conduct Earth monitoring through high-definition imagery and to amass extensive datasets pertaining to Earth observations. The scholarly articles contained within this reprint detail the latest progress in the domains of big data processing and the employment of artificial intelligence-based techniques for enhancing remote sensing technologies. |
| format | Online |
| id | doab-20.500.12854ir-137858 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1378582024-05-14T14:44:20Z Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II Jeon, Gwanggil hyperspectral image (HSI) classification transformer convolutional neural network (CNN) Sequencer long short-term memory network (LSTM) remote sensing object detection point representation sample quality assessment aerial target recognition center-ness quality radar echo extrapolation sequence-to-sequence (Seq2Seq) network 3D-Unet convective nowcasting hyperspectral unmixing spectral–spatial attention mechanism deep learning autoencoder moving point target low SNR transient disturbance temporal profile skip connection shifted window spatial feature extraction (SFE) spatial position encoding (SPE) geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model wildfire detection generative machine-learning stochastic modeling remote sensing segmentation uncertainty analysis deep neural network adversarial defense deep ensemble model unmanned aerial vehicle image recognition hyperspectral images classification network pruning multi-task optimization knowledge transfer multi-objective optimization 2D DOA estimation low-elevation-angle targets L-shaped uniform array L-shaped sparse array dilated convolutional autoencoder dilated convolutional neural network 3D convolution spatiotemporal fusion machine learning multi-source precipitation ConvLSTM F-SVD ionosphere peak height of F2 layer hmF2 prediction data sensor fusion extended Kalman filter lidar radar super-resolution remote sensing image convolutional neural network self-similarity gated recurrent unit ecological service value ecological–economic harmony driving mechanism thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues This publication elucidates the application of advanced technologies, including machine learning and deep learning, rooted in artificial intelligence, to the realm of remote sensing. It delineates the methodology employed to address prevailing challenges associated with the processing of images and image signals in remote sensing contexts. These methodologies are inherently computation-intensive, necessitating the utilization of high-performance computing apparatus, notably GPUs. With the evolution of such computational devices, alongside advancements in remote and aerial sensing technologies, it has become feasible to conduct Earth monitoring through high-definition imagery and to amass extensive datasets pertaining to Earth observations. The scholarly articles contained within this reprint detail the latest progress in the domains of big data processing and the employment of artificial intelligence-based techniques for enhancing remote sensing technologies. 2024-05-14T14:44:14Z 2024-05-14T14:44:14Z 2024 book ONIX_20240514_9783725807710_454 9783725807710 9783725807727 https://directory.doabooks.org/handle/20.500.12854/137858 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9098 https://mdpi.com/books/pdfview/book/9098 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0772-7 10.3390/books978-3-7258-0772-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725807710 9783725807727 336 open access |
| spellingShingle | hyperspectral image (HSI) classification transformer convolutional neural network (CNN) Sequencer long short-term memory network (LSTM) remote sensing object detection point representation sample quality assessment aerial target recognition center-ness quality radar echo extrapolation sequence-to-sequence (Seq2Seq) network 3D-Unet convective nowcasting hyperspectral unmixing spectral–spatial attention mechanism deep learning autoencoder moving point target low SNR transient disturbance temporal profile skip connection shifted window spatial feature extraction (SFE) spatial position encoding (SPE) geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model wildfire detection generative machine-learning stochastic modeling remote sensing segmentation uncertainty analysis deep neural network adversarial defense deep ensemble model unmanned aerial vehicle image recognition hyperspectral images classification network pruning multi-task optimization knowledge transfer multi-objective optimization 2D DOA estimation low-elevation-angle targets L-shaped uniform array L-shaped sparse array dilated convolutional autoencoder dilated convolutional neural network 3D convolution spatiotemporal fusion machine learning multi-source precipitation ConvLSTM F-SVD ionosphere peak height of F2 layer hmF2 prediction data sensor fusion extended Kalman filter lidar radar super-resolution remote sensing image convolutional neural network self-similarity gated recurrent unit ecological service value ecological–economic harmony driving mechanism thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title_full | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title_fullStr | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title_full_unstemmed | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title_short | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II |
| title_sort | advanced machine learning and deep learning approaches for remote sensing ii |
| topic | hyperspectral image (HSI) classification transformer convolutional neural network (CNN) Sequencer long short-term memory network (LSTM) remote sensing object detection point representation sample quality assessment aerial target recognition center-ness quality radar echo extrapolation sequence-to-sequence (Seq2Seq) network 3D-Unet convective nowcasting hyperspectral unmixing spectral–spatial attention mechanism deep learning autoencoder moving point target low SNR transient disturbance temporal profile skip connection shifted window spatial feature extraction (SFE) spatial position encoding (SPE) geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model wildfire detection generative machine-learning stochastic modeling remote sensing segmentation uncertainty analysis deep neural network adversarial defense deep ensemble model unmanned aerial vehicle image recognition hyperspectral images classification network pruning multi-task optimization knowledge transfer multi-objective optimization 2D DOA estimation low-elevation-angle targets L-shaped uniform array L-shaped sparse array dilated convolutional autoencoder dilated convolutional neural network 3D convolution spatiotemporal fusion machine learning multi-source precipitation ConvLSTM F-SVD ionosphere peak height of F2 layer hmF2 prediction data sensor fusion extended Kalman filter lidar radar super-resolution remote sensing image convolutional neural network self-similarity gated recurrent unit ecological service value ecological–economic harmony driving mechanism thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | hyperspectral image (HSI) classification transformer convolutional neural network (CNN) Sequencer long short-term memory network (LSTM) remote sensing object detection point representation sample quality assessment aerial target recognition center-ness quality radar echo extrapolation sequence-to-sequence (Seq2Seq) network 3D-Unet convective nowcasting hyperspectral unmixing spectral–spatial attention mechanism deep learning autoencoder moving point target low SNR transient disturbance temporal profile skip connection shifted window spatial feature extraction (SFE) spatial position encoding (SPE) geostatistical modeling multiple-point statistics uncertainty quantification subglacial topographic model hydrological model wildfire detection generative machine-learning stochastic modeling remote sensing segmentation uncertainty analysis deep neural network adversarial defense deep ensemble model unmanned aerial vehicle image recognition hyperspectral images classification network pruning multi-task optimization knowledge transfer multi-objective optimization 2D DOA estimation low-elevation-angle targets L-shaped uniform array L-shaped sparse array dilated convolutional autoencoder dilated convolutional neural network 3D convolution spatiotemporal fusion machine learning multi-source precipitation ConvLSTM F-SVD ionosphere peak height of F2 layer hmF2 prediction data sensor fusion extended Kalman filter lidar radar super-resolution remote sensing image convolutional neural network self-similarity gated recurrent unit ecological service value ecological–economic harmony driving mechanism thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20240514_9783725807710_454 |