Article
Geochemistry & Geophysics
Taner Ince, Nicolas Dobigeon
Summary: A weighted residual nonnegative matrix factorization method with spatial regularization is proposed to unmix hyperspectral data, aiming to improve the robustness of NMF against noise. Experimental results validate the effectiveness of the proposed method in providing spatial information for abundance matrix.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Xuelong Li, Xinxin Zhang, Yuan Yuan, Yongsheng Dong
Summary: Hyperspectral unmixing is an important research topic for spectral data analysis. Existing methods often treat similarity learning and unmixing as two separate steps, resulting in suboptimal similarity matrices. To address this issue, we propose an adaptive relationship preserving-based sparse NMF method that improves the performance and generalization ability of unmixing.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Ziyang Guo, Anyou Min, Bing Yang, Junhong Chen, Hong Li, Junbin Gao
Summary: This article proposes a new sparse oblique-manifold (OB) NMF method inspired by matrix manifold theory, treating the abundance matrix as located on the oblique manifold to eliminate constraints and incorporate intrinsic Riemannian geometry. By using the Riemannian conjugated gradient (RCG) algorithm and multiplicative iterative rule, the proposed method not only improves solution accuracy but also achieves a faster convergence rate. Experimental results demonstrate the effectiveness and efficiency of the proposed method compared to state-of-the-art NMF methods in HU.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Le Dong, Yuan Yuan, Xiaoqiang Lu
Summary: In this article, a novel unmixing method is proposed using two types of self-similarity to constrain sparse NMF. The method explores spatial global self-similarity groups between pixels based on the whole image and creates spectral local self-similarity groups inside superpixels. By sparsely encoding pixels within each spatial and spectral group, the method forces the abundance of pixels within each group to be similar, thereby constraining the NMF unmixing framework. Experiments demonstrate the superiority of this method over existing methods on synthetic and real data.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Kewen Qu, Zhenqing Li, Chenyang Wang, Fangzhou Luo, Wenxing Bao
Summary: Recently, graph learning methods have been studied extensively, but they often neglect the higher-order nearest-neighbor relation and the statistical properties of feature values in hyperspectral images, leading to limited spatial structure learning and hindered learning efficiency. To address these issues, this paper proposes a higher-order graph regularizer NMF with adaptive feature selection method. It introduces pixel second-order nearest-neighbor relation and combines it with the first-order relation to construct a higher-order graph regularizer, preserving the global spatial structure. Adaptive weight is introduced for data reconstruction to enhance the contribution of tiny features. Extensive experiments show the effectiveness and superiority of the proposed method compared with state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Fengchao Xiong, Jun Zhou, Shuyin Tao, Jianfeng Lu, Yuntao Qian
Summary: This article presents an end-to-end deep neural network named SNMF-Net for hyperspectral unmixing, which combines model-based and learning-based methods. SNMF-Net shows high physical interpretability and performance advantages over state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Environmental Sciences
Qin Jiang, Yifei Dong, Jiangtao Peng, Mei Yan, Yi Sun
Summary: The paper introduces a robust MLE-based NMF model for hyperspectral unmixing, which shows superior performance compared to existing NMF methods in experiments using simulated and real hyperspectral data sets.
Article
Remote Sensing
Heng-Chao Li, Shuang Liu, Xin-Ru Feng, Rui Wang, Yong-Jian Sun
Summary: This letter proposes a new double weighted sparse NTF (DWSNTF) unmixing method to make the most of spatial information and abundance sparsity. By utilizing a double weighted L-1 regularizer, more precise and sparse abundance maps can be characterized, while preserving more details and preventing oversmoothness. The experimental results demonstrate the validity and superiority of the proposed method against the state-of-the-art methods on both synthetic and real data.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Shaoquan Zhang, Guorong Zhang, Fan Li, Chengzhi Deng, Shengqian Wang, Antonio Plaza, Jun Li
Summary: The article introduces a new hyperspectral unmixing technique named SSWNMF, which can achieve accurate unmixing results by enhancing the sparsity of the solution and capturing the piecewise smooth structure of the data. This technique introduces two weighting factors and implements a multiplicative iterative strategy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Pan Zheng, Hongjun Su, Hongliang Lu, Qian Du
Summary: Hyperspectral unmixing with tensor models has gained attention recently. A novel adaptive hypergraph regularized multilayer sparse tensor factorization (AHGMLSTF) algorithm is proposed to overcome the physically uninterpretable results obtained from tensor-based decomposition. The algorithm incorporates a modified hypergraph that uses spectral angle distance (SAD) to better represent joint spatial and spectral information, and introduces the concept of multilayer decomposition to explore hierarchical features of hyperspectral images with a sparse constraint imposed on each layer.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Yuan Yuan, Zihan Zhang, Ganchao Liu
Summary: Hyperspectral remote sensing is an important method for earth observation, but the low spatial resolution of images makes it hard to distinguish ground objects. The HP-MLNMF framework proposed in this paper shows promising results in improving unmixing speed and efficiency in spectral signature estimation.
Article
Geochemistry & Geophysics
Jiangtao Peng, Weiwei Sun, Fan Jiang, Hong Chen, Yicong Zhou, Qian Du
Summary: This letter proposes a general loss-based NMF (GLNMF) model for hyperspectral unmixing. By introducing a robust loss function, the model improves its robustness and achieves higher accuracy compared to existing NMF methods, as demonstrated through experimental results on simulated and real data sets.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Mathematics, Applied
Yingying Xu, Lei Du, Haifeng Song, Songsong Dai
Summary: The significance of hyperspectral unmixing lies in separating mixed spectral signatures into endmembers and calculating their fractional abundances. However, blind unmixing based on non-negative matrix factorization (NMF) often yields inaccurate and unstable results, while semi-blind unmixing based on sparse regression requires an overcomplete spectral library. To overcome these limitations, a cooperative model called CoNMFSL is proposed, which combines NMF and spectral library to utilize partial prior information from the incomplete spectral library. Extensive simulations and real-data experiments demonstrate that CoNMFSL achieves more accurate and stable results compared to blind unmixing, and relaxes the application conditions of semi-blind unmixing.
JOURNAL OF NONLINEAR AND CONVEX ANALYSIS
(2022)
Article
Engineering, Electrical & Electronic
Ping Yang, Ting-Zhu Huang, Jie Huang, Jin-Ju Wang
Summary: The proposed method incorporates weighted nuclear norm and $L_{1/2}$ norm to consider the low-rank and sparse priors of each abundance map simultaneously in hyperspectral unmixing. An adaptive update mechanism is implemented to treat each constraint differently, leading to improved unmixing effect and solving speed.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Min Zhao, Tiande Gao, Jie Chen, Wei Chen
Summary: This study introduces an NMF-based unmixing framework that combines learned regularizers and handcrafted regularizers, demonstrating the integration of handcrafted regularizers using l2,1-norm as an example. The proposed framework is flexible and extendable, with effectiveness confirmed through both synthetic and real airborne data.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)