Results 71 to 80 of about 265 (159)
Expert Systems, Volume 41, Issue 10, October 2024.Abstract Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance.
Abdenour Hadid +2 more
wiley +1 more source
RGB‐guided hyperspectral image super‐resolution with deep progressive learning
CAAI Transactions on Intelligence Technology, Volume 9, Issue 3, Page 679-694, June 2024.Abstract Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance.
Tao Zhang +5 more
wiley +1 more source
Robust low-rank abundance matrix estimation for hyperspectral unmixing
The Journal of Engineering, 2019 Hyperspecral unmixing (HU) is one of the crucial steps of hyperspectral image (HSI) processing. The process of HU can be divided into end-member extraction and abundance estimation.
Fan Feng +4 more
doaj +1 more source
DNMF-AG: A Sparse Deep NMF Model with Adversarial Graph Regularization for Hyperspectral Unmixing
Remote SensingHyperspectral unmixing (HU) aims to extract constituent information from mixed pixels and is a fundamental task in hyperspectral remote sensing. Deep non-negative matrix factorization (DNMF) has recently attracted attention for HU due to its hierarchical
Kewen Qu, Xiaojuan Luo, Wenxing Bao
doaj +1 more source
Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2018 Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization
Zuoyu Zhang +4 more
doaj +1 more source
Endmember Independence and Bilateral Filtering Regularizations for Blind Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral unmixing (HU) aims to decompose the mixed pixels of a hyperspectral image into endmembers weighted by their corresponding abundances. Recently, matrix–vector nonnegative tensor factorization (MV-NTF) has been successfully applied to ...
Yang Hu, Lei Sun, Ziyang Zhang, Feng Xie
doaj +1 more source
IEEE Access, 2019 Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances.
Li Sun +3 more
doaj +1 more source
Hyperspectral Unmixing with Bandwise Generalized Bilinear Model
, 2018 Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of ...
Liu, Yu +13 more
core +1 more source
DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing
, 2023 International audienceRecently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs).
Farah, Imed Riadh +3 more
core +1 more source
SSTNT: A Spatial–Spectral Similarity Guided Transformer-in-Transformer for Hyperspectral Unmixing
PhotonicsVision Transformers (ViTs), owing to their strong capability in modeling global contextual dependencies, have been widely adopted in hyperspectral image unmixing (HU).
Xinyu Cui +3 more
doaj +1 more source