Results 21 to 30 of about 1,956 (163)

GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness

European Journal of Remote Sensing, 2023
This study introduces GAUSS (Guided encoder-decoder Architecture for hyperspectral Unmixing with Spatial Smoothness), a novel autoencoder-based architecture for hyperspectral unmixing (HU).
H.M.K.D. Wickramathilaka, D. Fernando, D. Jayasundara, D. Wickramasinghe, D.Y.L. Ranasinghe, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, H.M.V.R. Herath, L. Ramanayake, N. Senarath, H.M.H.K. Weerasooriya   +10 more
doaj   +1 more source

A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu, Linlin Wang, Wen Zeng, Qingfeng Guan, Zhen Hu   +4 more
doaj   +1 more source

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

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, Menghua Wang, Chuanmin Hu, Douglas G. Capone, Ajit Subramaniam, Edward J. Carpenter, Yuyuan Xie   +6 more
wiley   +1 more source