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230202 ||| eng |
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|a 9783036557953
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|a 9783036557960
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|a books978-3-0365-5796-0
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| 100 |
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|a Chang, Chein-I
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| 245 |
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|a Advances in Hyperspectral Data Exploitation
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (434 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a denoising
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| 653 |
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|a spatial filter
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| 653 |
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|a deep convolutional neural networks
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| 653 |
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|a unmanned aerial vehicles (UAVs)
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| 653 |
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|a spatial measurement
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| 653 |
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|a evolutionary computation
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| 653 |
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|a data augmentation
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| 653 |
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|a hyperspectral
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| 653 |
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|a plug-and-play
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| 653 |
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|a FTIR
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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 heuristic algorithms
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| 653 |
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|a constrained energy minimization (CEM)
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| 653 |
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|a vegetation
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| 653 |
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|a SFIM
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a underwater hyperspectral target detection
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| 653 |
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|a joint tensor decomposition
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| 653 |
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|a mine environment
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a emissivity
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| 653 |
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|a lightweight convolutional neural networks
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| 653 |
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|a superpixel segmentation
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| 653 |
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|a hyperspectral images
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| 653 |
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|a spectral-spatial residual network
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| 653 |
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|a carbon dioxide absorption
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| 653 |
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|a target detection
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| 653 |
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|a color formation models
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| 653 |
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|a attention mechanism
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| 653 |
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|a image fusion
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| 653 |
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|a hyperspectral imaging
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| 653 |
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|a constrained sparse representation
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| 653 |
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|a nonlinear unmixing
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| 653 |
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|a hyperspectral imagery
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| 653 |
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|a channel augmented attention
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| 653 |
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|a hyperspectral image
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| 653 |
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|a visualization
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| 653 |
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|a rice leaf folder
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| 653 |
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|a fused features
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| 653 |
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|a multiscale decision fusion
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| 653 |
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|a rice
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| 653 |
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|a moving target detection
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| 653 |
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|a band selection (BS)
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| 653 |
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|a temperature
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| 653 |
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|a image classification
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| 653 |
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|a coffee beans
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| 653 |
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|a anomaly detection
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| 653 |
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|a hyperspectral unmixing
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| 653 |
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|a relation network
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| 653 |
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|a rice leaf blast
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| 653 |
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|a change detection
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| 653 |
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|a band selection
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| 653 |
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|a upland swamps
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| 653 |
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|a residual augmented attentional u-shape network
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| 653 |
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|a least square estimation
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| 653 |
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|a generative adversarial network
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| 653 |
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|a hyperspectral image classification
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| 653 |
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|a hyperspectral reconstruction
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| 653 |
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|a constrained-target optimal index factor band selection (CTOIFBS)
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| 653 |
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|a hyperspectral imagery classification
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| 653 |
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|a hyperspectral remote sensing
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| 653 |
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|a hyperspectral imaging data
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| 653 |
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|a constraint representation
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| 653 |
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|a hyperspectral image super-resolution
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| 653 |
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|a transfer learning
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| 653 |
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|a underwater spectral imaging system
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| 653 |
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|a spatial augmented attention
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| 653 |
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|a separation
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| 653 |
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|a boundary-aware constraint
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| 653 |
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|a hyperspectral target detection
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| 653 |
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|a MWIR
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| 653 |
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|a spatio-temporal processing
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| 653 |
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|a air temperature
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| 653 |
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|a insect damage
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| 653 |
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|a multi-source image fusion
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| 653 |
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|a classification
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| 653 |
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|a spectral reconstruction
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| 653 |
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|a vegetation mapping
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| 653 |
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|a meta-learning
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| 653 |
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|a midwave infrared
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| 653 |
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|a convolutional neural network
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| 653 |
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|a atmospheric transmittance
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| 653 |
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|a multispectral image
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| 653 |
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|a hyperspectral imaging (HSI)
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| 653 |
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|a hyperspectral image few-shot classification
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| 653 |
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|a self-supervised training
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| 653 |
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|a self-supervised learning
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| 700 |
1 |
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|a Song, Meiping
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| 700 |
1 |
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|a Yu, Chunyan
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| 700 |
1 |
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|a Wang, Yulei
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| 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
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-5796-0
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| 856 |
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2 |
|u https://directory.doabooks.org/handle/20.500.12854/94557
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/6392
|7 0
|x Verlag
|3 Volltext
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|a 363
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|a Using hyperspectral imaging (HSI) to exploit data has been found in a wide variety of applications. This reprint book only presents a small glimpse of it. Many other important applications using HSI which have emerged in data exploitation are not covered in this reprint book. For example, such applications may include water pollution and toxic waste in environmental monitoring, pesticide residual detection in food safety and inspection, plant and crop disease detection in agriculture, tumor detection and breast cancer detection in medical imaging, drug traffic in law enforcement, etc. Nevertheless, this reprint book provides many techniques which may find their ways in these applications as well.
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