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230811 ||| eng |
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|a books978-3-0365-7947-4
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|a 9783036579474
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|a 9783036579467
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| 100 |
1 |
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|a Jeon, Gwanggil
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| 245 |
0 |
0 |
|a Advanced Machine Learning and Deep Learning Approaches for Remote Sensing
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (362 p.)
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| 653 |
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|a retinex theory
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| 653 |
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|a discriminative representation learning
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| 653 |
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|a cloud matting
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| 653 |
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|a multi-dimensional prediction model
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| 653 |
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|a atmospheric turbulence
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| 653 |
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|a solar panel
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| 653 |
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|a live fuel moisture content
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| 653 |
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|a two-step convolution model
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| 653 |
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|a capacity estimation
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| 653 |
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|a multimodal
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| 653 |
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|a cloud removal
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| 653 |
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|a dilated convolution
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| 653 |
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|a fine-grained image classification
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| 653 |
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|a maritime communication
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| 653 |
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|a n/a
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| 653 |
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|a remote-sensing
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| 653 |
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|a vehicle detection
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| 653 |
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|a significant wave height
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| 653 |
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|a autoencoder
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| 653 |
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|a solar farm
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| 653 |
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|a Transformer
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| 653 |
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|a data fusion
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| 653 |
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|a deep learning
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| 653 |
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|a optical remote sensing
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| 653 |
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|a LSTM
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| 653 |
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|a improved transformer encoder
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| 653 |
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|a peri-urban forests
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| 653 |
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|a few-shot learning
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| 653 |
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|a FlexibleNet
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| 653 |
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|a evaluation methods
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| 653 |
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|a ensemble learning
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| 653 |
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|a altimeter
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| 653 |
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|a spatiotemporal fusion
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| 653 |
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|a lightweight convolutional neural network
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| 653 |
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|a turbulence degradation
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| 653 |
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|a mode detection
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| 653 |
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|a principal component analysis
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| 653 |
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|a attention mechanism
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| 653 |
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|a sea surface temperature
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| 653 |
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|a interdimensional attention
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| 653 |
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|a multiscale feature fusion
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| 653 |
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|a Research & information: general / bicssc
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| 653 |
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|a spectrogram augmentation
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| 653 |
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|a digital surface model
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| 653 |
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|a curriculum learning
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| 653 |
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|a improved Tversky loss
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| 653 |
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|a remote image
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| 653 |
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|a self-attention
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| 653 |
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|a Gaussian process regression
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| 653 |
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|a photovoltaics
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| 653 |
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|a image reconstruction
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| 653 |
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|a image super-resolution
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| 653 |
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|a remote sensing
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| 653 |
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|a CNN
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| 653 |
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|a cloud detection
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| 653 |
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|a multi-source remote sensing
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| 653 |
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|a computer vision
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| 653 |
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|a carbon sequestration
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| 653 |
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|a semi-supervised learning
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| 653 |
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|a reconstruction refinement
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| 653 |
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|a SAR target recognition
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| 653 |
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|a depthwise separable convolutional neural networks
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| 653 |
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|a semantic segmentation
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| 653 |
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|a Geography / bicssc
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| 653 |
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|a noise suppression deblurring
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| 653 |
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|a feature separation
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| 653 |
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|a model design
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| 653 |
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|a global correlation information
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| 653 |
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|a SAR
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| 653 |
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|a convolutional neural network
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| 653 |
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|a attention pyramid
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| 653 |
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|a orbital angular momentum
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| 653 |
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|a robust deep learning
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| 653 |
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|a evaporation duct
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| 653 |
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|a sound detection
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| 653 |
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|a multi-scale supervision
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| 653 |
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|a mutual information
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| 653 |
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|a low-light image enhancement
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| 700 |
1 |
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|a Jeon, Gwanggil
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-7947-4
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/101386
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/7482
|7 0
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 000
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| 082 |
0 |
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|a 380
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| 520 |
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|a 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.
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