Results 71 to 80 of about 828,208 (170)

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, Baojun Zhao, Linbo Tang, Wenzheng Wang, Sen Jia   +4 more
doaj   +1 more source

Compressed sensing based hyperspectral unmixing

, 2014
In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based ...
Albayrak, R. Tufan, GÜRBÜZ, Ali Cafer, R. Tufan Albayrak, Ali Cafer Gurbuz, Gunyel, Bertan, Bertan Gunyel   +5 more
core   +1 more source

DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing

ICT Express
Hyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects.
Suresh Aala, Prudhvi Krishna Pavuluri, Anuj Deshpande, Sravan Kumar Sikhakolli, Karthikeyan Elumalai, Sunil Chinnadurai, Eswar Panchakarla, Md. Abdul Latif Sarker, Dong Seog Han   +8 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, Ying Fu, Liwei Huang, Siyuan Li, Shaodi You, Chenggang Yan   +5 more
wiley   +1 more source

DNMF-AG: A Sparse Deep NMF Model with Adversarial Graph Regularization for Hyperspectral Unmixing

Remote Sensing
Hyperspectral 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, Shouyi Liao, Hexin Zhang, Shicheng Wang, Yongchao Wang   +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 Sensing
Hyperspectral 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

A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or ...
Mingle Zhang, Mingyu Yang, Hongyu Xie, Pinliang Yue, Wei Zhang, Qingbin Jiao, Liang Xu, Xin Tan   +7 more
doaj   +1 more source

Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing

MATEC Web of Conferences, 2018
As a powerful and explainable blind separation tool, non-negative matrix factorization (NMF) is attracting increasing attention in Hyperspectral Unmixing(HU).
Ning ShangBin, Zuo FengChao
doaj   +1 more source

Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

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, Kang Zhao, Congying Han, Ziwen Liu   +3 more
doaj   +1 more source