Results 11 to 20 of about 828,208 (170)

Endmember-Free Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu, Longfei Hou, Weiming Zheng, Nazik Alturki, Jamel Baili, Weili Kong, Jiaming Pei   +6 more
doaj   +2 more sources

Illumination invariance and shadow compensation on hyperspectral images [PDF]

, 2014
To obtain intrinsic reflectance of the scene by hyperspectral imaging systems has been a scientific and engineering challenge. Factors such as illumination variations, atmospheric effects and viewing geometries are common artefacts which modulate the way
Ibrahim, Izzati
core   +7 more sources

Multi-stage convolutional autoencoder network for hyperspectral unmixing

International Journal of Applied Earth Observations and Geoinformation, 2022
Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and
Yang Yu, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, Hao Li   +5 more
doaj   +1 more source

Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

Remote Sensing, 2023
In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years.
Burkni Palsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
doaj   +1 more source

Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Deep learning (DL) has heavily impacted the data-intensive field of remote sensing. Autoencoders are a type of DL methods that have been found to be powerful for blind hyperspectral unmixing (HU). HU is the process of resolving the measured spectrum of a
Burkni Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source

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

Journal 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

Applied 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, Yunfei Zhu, Wei Huang, Qiqiang Chen, Sijia Li   +4 more
doaj   +1 more source

A Modified Huber Nonnegative Matrix Factorization Algorithm for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hypersepctral unmixing (HU) has been one of the most challenging tasks in hyperspectral image research. Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior ...
Ziyang Guo, Anyou Min, Bing Yang, Junhong Chen, Hong Li   +4 more
doaj   +1 more source

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

European 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, Tongyu Xu, Shuai Feng, Fenghua Yu, Kai Song   +4 more
doaj   +1 more source

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

, 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, Jocelyn Chanussot, Qian Du, Gader, Paul, Antonio Plaza, José M. Bioucas-dias, Chanussot, Jocelyn, Dobigeon, Nicolas, Plaza, Antonio, Du, Qian, Mario Parente, Bioucas-Dias, José M., Nicolas Dobigeon, Parente, Mario   +13 more
core   +1 more source