Results 21 to 30 of about 828,208 (170)
Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE 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
Geophysical 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 spectral unmixing of hyperspectral images using Gaussian processes [PDF]
, 2012 This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components ...
Altmann, Yoann +5 more
core +1 more source
High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems
Geoscience 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
Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]
, 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
IET Image Processing, Volume 16, Issue 13, Page 3557-3566, 13 November 2022., 2022 Abstract Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral images, unmixing technology is a key processing technology, such as linear mixing model and its derived ...
Haoyang Yu +5 more
wiley +1 more source
Nonlinear unmixing of hyperspectral images using a generalized bilinear model [PDF]
, 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
A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing
Remote Sensing, 2019 Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level.
Jinlin Zou, Jinhui Lan
doaj +1 more source
Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection [PDF]
, 2014 This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional ...
Altmann, Yoann +10 more
core +1 more source
Remote Sensing, 2023 Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner.
Chunyu Li, Rong Cai, Junchuan Yu
doaj +1 more source