Results 81 to 90 of about 919,951 (170)
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
Multiple graph regularized NMF for hyperspectral unmixing
, 2015 Hyperspectral unmixing is an important technique for estimating fraction of different land covers from remote sensing imagery. In recent years, nonnegative matrix factorization (NMF) methods with various constraints have been introduced into ...
Lei Tong +7 more
core +1 more source
Graph-based Hyperspectral Unmixing Using Deep Convolutional Neural Networks [PDF]
, 2022 Hyperspectral image unmixing (HU) is a remote sensing technique that has numerous applications in ecology, earth observation, and agronomy. Recently the research surrounding hyperspectral unmixing are using deep convolutional neural networks (CNNs). This
Zamiri-Jafarian, Mohammadkia
core
NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing
, 2021 Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming.
Jun Zhou +9 more
core +1 more source
DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThe pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing technology. Hyperspectral unmixing (HU) techniques serve as effective means to address this issue.
Hao Chen +4 more
doaj +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
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
Contrastive Learning approach for blind Hyperspectral Unmixing (CLHU)
, 2023 The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input ...
Himar Fabelo (16724360) +9 more
core +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