Deep spatial-temporal networks for flame detection
Sign Up to like & getrecommendations! 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… read more here.
Keywords: detection; deep spatial; temporal networks; spatial temporal ... See more keywords
Deep spatial representation learning of polyamide nanofiltration membranes
Sign Up to like & getrecommendations! 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… read more here.
Keywords: nanofiltration; spatial representation; polyamide nanofiltration; representation learning ... See more keywords
Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-97074-4
Abstract: Landslides are among the geological disasters that frequently occur worldwide and significantly restrict the sustainable development of society. Therefore, it is of great practical significance to perform landslide susceptibility assessment. In addressing issues such as… read more here.
Keywords: deep spatial; susceptibility; landslide susceptibility; susceptibility assessment ... See more keywords
A Hybrid Spectral Attention-Enabled Multiscale Spatial–Spectral Learning Network for Hyperspectral Image Superresolution
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2024.3407953
Abstract: Hyperspectral image superresolution (HSI-SR) has become an essential step of data preprocessing for tasks such as classification and change detection in remote sensing. For HSI-SR tasks, the state-of-the-art methods lie in how to learn effective… read more here.
Keywords: deep spatial; hyperspectral image; spectral features; image superresolution ... See more keywords
Unsupervised Scale-Driven Change Detection With Deep Spatial–Spectral Features for VHR Images
Sign Up to like & getrecommendations! 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… read more here.
Keywords: change; driven change; scale driven; unsupervised scale ... See more keywords
Toward Effective Hyperspectral Image Classification Using Dual-Level Deep Spatial Manifold Representation
Sign Up to like & getrecommendations! 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.,… read more here.
Keywords: classification; level deep; deep spatial; dual level ... See more keywords
Cooperated Spectral Low-Rankness Prior and Deep Spatial Prior for HSI Unsupervised Denoising
Sign Up to like & getrecommendations! 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… read more here.
Keywords: rankness prior; deep spatial; hsi; low rankness ... See more keywords
Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification
Sign Up to like & getrecommendations! Published in 2024 at "Frontiers in Computational Neuroscience"
DOI: 10.3389/fncom.2024.1423051
Abstract: The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the… read more here.
Keywords: classification; deep spatial; brain; feature fusion ... See more keywords