Results 31 to 40 of about 919,951 (170)

Hyperspectral image non-linear unmixing using joint extrinsic and intrinsic priors with L1/2-norms to non-negative matrix factorisation

open access: yesJournal of Spectral Imaging, 2022
Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance.
K. Priya, K. K. Rajkumar
doaj   +1 more source

An Efficient Attention-Based Convolutional Neural Network That Reduces the Effects of Spectral Variability for Hyperspectral Unmixing

open access: yesApplied Sciences, 2022
The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin   +4 more
doaj   +1 more source

Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

open access: yesEuropean Journal of Remote Sensing, 2022
Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of ...
Qiang Guan   +4 more
doaj   +1 more source

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]

open access: yes, 2012
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Paul Gader   +13 more
core   +1 more source

Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (
Xiang Xu   +3 more
doaj   +1 more source

Trichodesmium Around Australia: A View From Space

open access: yesGeophysical Research Letters, Volume 50, Issue 16, 28 August 2023., 2023
Abstract The cyanobacterium Trichodesmium is responsible for approximately half of the ocean's nitrogen input through nitrogen fixation. Although it was first recorded near Australia in the 18th century, the knowledge of where and when large quantity of Trichodesmium around Australia could be found is still lacking.
Lin Qi   +6 more
wiley   +1 more source

Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]

open access: yes, 2013
When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid   +14 more
core   +1 more source

High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems

open access: yesGeoscience Data Journal, Volume 9, Issue 2, Page 221-234, November 2022., 2022
Hyperspectral remote sensing has been developed to detect individual absorption features related to specific chemical bonds in soils, liquids or gases; however, because UAV‐based pushbroom hyperspectral sensor technologies are relatively new, no public datasets are currently available.
Jae‐In Kim   +7 more
wiley   +1 more source

Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery [PDF]

open access: yes, 2011
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise.
Altmann, Yoann   +9 more
core   +1 more source

Nonlinear unmixing of hyperspectral images using a generalized bilinear model [PDF]

open access: yes, 2011
Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of
Altmann, Yoann   +9 more
core   +1 more source

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