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...

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Бібліографічні деталі
Формат:	Online
Мова:	Англійська
Опубліковано:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Предмети:	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
Онлайн доступ:	ONIX_20240514_9783725807710_454
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Резюме:	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.

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II

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