Advanced Machine Learning and Deep Learning Approaches for Remote Sensing
This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These technique...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20230714_9783036579467_85 |
| الوسوم: |
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| _version_ | 1863742663149223936 |
|---|---|
| collection | Directory of Open Access Books |
| description | This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology. |
| format | Online |
| id | doab-20.500.12854ir-101386 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1013862024-03-28T03:31:36Z Advanced Machine Learning and Deep Learning Approaches for Remote Sensing Jeon, Gwanggil live fuel moisture content deep learning ensemble learning multi-source remote sensing spatiotemporal fusion dilated convolution improved transformer encoder global correlation information semantic segmentation attention mechanism robust deep learning remote sensing data fusion low-light image enhancement retinex theory remote-sensing orbital angular momentum mode detection fine-grained image classification attention pyramid atmospheric turbulence sea surface temperature mutual information LSTM self-attention interdimensional attention noise suppression deblurring curriculum learning image reconstruction turbulence degradation depthwise separable convolutional neural networks spectrogram augmentation sound detection vehicle detection image super-resolution model design evaluation methods maritime communication evaporation duct multi-dimensional prediction model digital surface model multimodal multi-scale supervision feature separation reconstruction refinement significant wave height autoencoder principal component analysis SAR altimeter Gaussian process regression convolutional neural network computer vision solar farm solar panel capacity estimation photovoltaics optical remote sensing peri-urban forests lightweight convolutional neural network FlexibleNet carbon sequestration semi-supervised learning few-shot learning SAR target recognition discriminative representation learning remote image CNN multiscale feature fusion Transformer improved Tversky loss two-step convolution model cloud detection cloud matting cloud removal n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography This reprint provides research on how technologies such as artificial intelligence-based machine learning and deep learning can be applied to remote sensing. Through this, we can see the process of solving the existing problems of image and image signal processing for remote sensing. These techniques are computationally intensive and require the help of high-performance computing devices. With the development of devices such as GPUs, remote sensing technology, and aerial sensing technology, it is possible to monitor the Earth with high-resolution images and to obtain vast amounts of Earth observation data. The papers published in this reprint describe recent advances in big data processing and artificial intelligence-based technologies for remote sensing technology. 2023-07-14T14:29:04Z 2023-07-14T14:29:04Z 2023 book ONIX_20230714_9783036579467_85 9783036579467 9783036579474 https://directory.doabooks.org/handle/20.500.12854/101386 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7482 https://mdpi.com/books/pdfview/book/7482 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-7947-4 10.3390/books978-3-0365-7947-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036579467 9783036579474 362 Basel open access |
| spellingShingle | live fuel moisture content deep learning ensemble learning multi-source remote sensing spatiotemporal fusion dilated convolution improved transformer encoder global correlation information semantic segmentation attention mechanism robust deep learning remote sensing data fusion low-light image enhancement retinex theory remote-sensing orbital angular momentum mode detection fine-grained image classification attention pyramid atmospheric turbulence sea surface temperature mutual information LSTM self-attention interdimensional attention noise suppression deblurring curriculum learning image reconstruction turbulence degradation depthwise separable convolutional neural networks spectrogram augmentation sound detection vehicle detection image super-resolution model design evaluation methods maritime communication evaporation duct multi-dimensional prediction model digital surface model multimodal multi-scale supervision feature separation reconstruction refinement significant wave height autoencoder principal component analysis SAR altimeter Gaussian process regression convolutional neural network computer vision solar farm solar panel capacity estimation photovoltaics optical remote sensing peri-urban forests lightweight convolutional neural network FlexibleNet carbon sequestration semi-supervised learning few-shot learning SAR target recognition discriminative representation learning remote image CNN multiscale feature fusion Transformer improved Tversky loss two-step convolution model cloud detection cloud matting cloud removal n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title_full | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title_fullStr | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title_full_unstemmed | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title_short | Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| title_sort | advanced machine learning and deep learning approaches for remote sensing |
| topic | live fuel moisture content deep learning ensemble learning multi-source remote sensing spatiotemporal fusion dilated convolution improved transformer encoder global correlation information semantic segmentation attention mechanism robust deep learning remote sensing data fusion low-light image enhancement retinex theory remote-sensing orbital angular momentum mode detection fine-grained image classification attention pyramid atmospheric turbulence sea surface temperature mutual information LSTM self-attention interdimensional attention noise suppression deblurring curriculum learning image reconstruction turbulence degradation depthwise separable convolutional neural networks spectrogram augmentation sound detection vehicle detection image super-resolution model design evaluation methods maritime communication evaporation duct multi-dimensional prediction model digital surface model multimodal multi-scale supervision feature separation reconstruction refinement significant wave height autoencoder principal component analysis SAR altimeter Gaussian process regression convolutional neural network computer vision solar farm solar panel capacity estimation photovoltaics optical remote sensing peri-urban forests lightweight convolutional neural network FlexibleNet carbon sequestration semi-supervised learning few-shot learning SAR target recognition discriminative representation learning remote image CNN multiscale feature fusion Transformer improved Tversky loss two-step convolution model cloud detection cloud matting cloud removal n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| topic_facet | live fuel moisture content deep learning ensemble learning multi-source remote sensing spatiotemporal fusion dilated convolution improved transformer encoder global correlation information semantic segmentation attention mechanism robust deep learning remote sensing data fusion low-light image enhancement retinex theory remote-sensing orbital angular momentum mode detection fine-grained image classification attention pyramid atmospheric turbulence sea surface temperature mutual information LSTM self-attention interdimensional attention noise suppression deblurring curriculum learning image reconstruction turbulence degradation depthwise separable convolutional neural networks spectrogram augmentation sound detection vehicle detection image super-resolution model design evaluation methods maritime communication evaporation duct multi-dimensional prediction model digital surface model multimodal multi-scale supervision feature separation reconstruction refinement significant wave height autoencoder principal component analysis SAR altimeter Gaussian process regression convolutional neural network computer vision solar farm solar panel capacity estimation photovoltaics optical remote sensing peri-urban forests lightweight convolutional neural network FlexibleNet carbon sequestration semi-supervised learning few-shot learning SAR target recognition discriminative representation learning remote image CNN multiscale feature fusion Transformer improved Tversky loss two-step convolution model cloud detection cloud matting cloud removal n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| url | ONIX_20230714_9783036579467_85 |