Results 11 to 20 of about 265 (159)

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
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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
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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
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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
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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
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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
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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
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SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.
Bin Wang, Huizheng Yao, Dongmei Song, Jie Zhang, Han Gao   +4 more
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Mamba-based spatial-spectral fusion network for hyperspectral unmixing

Journal of King Saud University: Computer and Information Sciences
Hyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values.
Yuquan Gan, Jingtao Wei, Mengmeng Xu
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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   +2 more sources