Results 21 to 30 of about 151 (115)

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, Jun Li, Shutao Li, Antonio Plaza   +3 more
doaj   +1 more source

Comparative Analysis of Automated Text Summarization Techniques: The Case of Ethiopian Languages

Wireless Communications and Mobile Computing, Volume 2022, Issue 1, 2022., 2022
Nowadays, there is an abundance of information available from both online and offline sources. For a single topic, we can get more than hundreds of sources containing a wealth of information. The ability to extract or generate a summary of popular content allows users to quickly search for content and obtain preliminary data in the shortest amount of ...
Wubetu Barud Demilie, Danfeng Hong
wiley   +1 more source

Amazon Hydrology From Space: Scientific Advances and Future Challenges

Reviews of Geophysics, Volume 59, Issue 4, December 2021., 2021
Abstract As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite‐based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era.
Alice César Fassoni‐Andrade, Ayan Santos Fleischmann, Fabrice Papa, Rodrigo Cauduro Dias de Paiva, Sly Wongchuig, John M. Melack, Adriana Aparecida Moreira, Adrien Paris, Anderson Ruhoff, Claudio Barbosa, Daniel Andrade Maciel, Evlyn Novo, Fabien Durand, Frédéric Frappart, Filipe Aires, Gabriel Medeiros Abrahão, Jefferson Ferreira‐Ferreira, Jhan Carlo Espinoza, Leonardo Laipelt, Marcos Heil Costa, Raul Espinoza‐Villar, Stéphane Calmant, Victor Pellet   +22 more
wiley   +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

Tracing Carbonate Formation, Serpentinization, and Biological Materials With Micro‐/Meso‐Scale Infrared Imaging Spectroscopy in a Mars Analog System, Samail Ophiolite, Oman

Earth and Space Science, Volume 8, Issue 11, November 2021., 2021
Abstract Visible‐shortwave infrared (VSWIR) imaging spectrometers map composition remotely with spatial context, typically at many meters‐scale from orbital and airborne data. Here, we evaluate VSWIR imaging spectroscopy capabilities at centimeters to sub‐millimeter scale at the Samail Ophiolite, Oman, where mafic and ultramafic lithologies and their ...
Ellen K. Leask, Bethany L. Ehlmann, Rebecca N. Greenberger, Patrick Pinet, Yves Daydou, Georges Ceuleneer, Peter Kelemen   +6 more
wiley   +1 more source

An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification

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

A Hierarchical Sparsity Unmixing Method to Address Endmember Variability in Hyperspectral Image

Remote Sensing, 2018
With a low spectral resolution hyperspectral sensor, the signal recorded from a given pixel against the complex background is a mixture of spectral contents. To improve the accuracy of classification and subpixel object detection, hyperspectral unmixing (
Jinlin Zou, Jinhui Lan, Yang Shao
doaj   +1 more source

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances.
E. M. M. B. Ekanayake, H. M. H. K. Weerasooriya, D. Y. L. Ranasinghe, S. Herath, B. Rathnayake, G. M. R. I. Godaliyadda, M. P. B. Ekanayake, H. M. V. R. Herath   +7 more
doaj   +1 more source

Deep convolutional transformer network for hyperspectral unmixing

European Journal of Remote Sensing, 2023
Hyperspectral unmixing (HU) is considered one of the most important ways to improve hyperspectral image analysis. HU aims to break down the mixed pixel into a set of spectral signatures, often commonly referred to as endmembers, and determine the ...
Fazal Hadi, Jingxiang Yang, Ghulam Farooque, Liang Xiao   +3 more
doaj   +1 more source

Hyperspectral Unmixing Using Robust Deep Nonnegative Matrix Factorization

Remote Sensing, 2023
Nonnegative matrix factorization (NMF) and its numerous variants have been extensively studied and used in hyperspectral unmixing (HU). With the aid of the designed deep structure, deep NMF-based methods demonstrate advantages in exploring the ...
Risheng Huang, Huiyun Jiao, Xiaorun Li, Shuhan Chen, Chaoqun Xia   +4 more
doaj   +1 more source