Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (336 p.)Innehållstyp: - text
- computer
- online resource
- 9783725807710
- 9783725807727
- Technology, Engineering, Agriculture, Industrial processes
- Technology: general issues
- 2D DOA estimation
- 3D convolution
- 3D-Unet
- ConvLSTM
- F-SVD
- L-shaped sparse array
- L-shaped uniform array
- Sequencer
- adversarial defense
- aerial target recognition
- autoencoder
- center-ness quality
- convective nowcasting
- convolutional neural network
- convolutional neural network (CNN)
- data sensor fusion
- deep ensemble model
- deep learning
- deep neural network
- dilated convolutional autoencoder
- dilated convolutional neural network
- driving mechanism
- ecological service value
- ecological–economic harmony
- extended Kalman filter
- gated recurrent unit
- generative machine-learning
- geostatistical modeling
- hmF2
- hydrological model
- hyperspectral image (HSI) classification
- hyperspectral images classification
- hyperspectral unmixing
- image recognition
- ionosphere
- knowledge transfer
- lidar
- long short-term memory network (LSTM)
- low SNR
- low-elevation-angle targets
- machine learning
- moving point target
- multi-objective optimization
- multi-source precipitation
- multi-task optimization
- multiple-point statistics
- network pruning
- peak height of F2 layer
- p
Open Access Unrestricted online access star
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.
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eng
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