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Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-10079-1
Abstract: Every year, fire accidents cause substantial economic losses and casualties. Being able to detect a fire at the early stage is the only way to avoid notable disasters. Although conventional fire alarm systems (CFAs) that…
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Keywords:
detection;
deep spatial;
temporal networks;
spatial temporal ... See more keywords
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Published in 2020 at "Journal of Membrane Science"
DOI: 10.1016/j.memsci.2020.118910
Abstract: Abstract Machine learning overfitting caused by data scarcity greatly limits the application of chemical artificial intelligence in membrane materials. As the original data for thin film polyamide nanofiltration membranes is limited, here we propose to…
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Keywords:
nanofiltration;
spatial representation;
polyamide nanofiltration;
representation learning ... See more keywords
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Published in 2020 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2020.2968098
Abstract: The rapid development of remote sensing technology has enabled the acquisition of very high spatial resolution (VHR) multitemporal images in Earth observation. However, how to effectively exploit these existing data to accurately monitor land surface…
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Keywords:
change;
driven change;
scale driven;
unsupervised scale ... See more keywords
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Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3073932
Abstract: Hyperspectral image (HSI) contains an abundant spatial structure that can be embedded into feature extraction (FE) or classifier (CL) components for pixelwise classification enhancement. Although some existing works have exploited some simple spatial structures (e.g.,…
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Keywords:
classification;
level deep;
deep spatial;
dual level ... See more keywords
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Published in 2022 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2022.3211471
Abstract: Model-driven methods and data-driven methods have been widely developed for hyperspectral image (HSI) denoising. However, there are pros and cons in both model-driven and data-driven methods. To address this issue, we develop a self-supervised HSI…
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Keywords:
rankness prior;
deep spatial;
hsi;
low rankness ... See more keywords