- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Nonconvex-sparsity and Nonlocal-smoothness Based Blind Hyperspectral Unmixing
摘要： Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the non-local total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.
关键词： log-sum penalty,non-negative matrix factorization,non-local total variation regularization,blind unmixing,Hyperspectral imaging
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Blind Nonlinear Hyperspectral Unmixing Using an <tex>$\ell_{q}$</tex> Regularizer
摘要： Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an lq regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.
关键词： non-negative matrix factorization,Spectral unmixing,bilinear model,hyperspectral images
更新于2025-09-23 15:22:29
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Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications
摘要： Motivation: Non-negative matrix factorization (NMF) is a common tool for obtaining low-rank approximations of non-negative data matrices and has been widely used in machine learning, e.g., for supporting feature extraction in high-dimensional classification tasks. In its classical form NMF is an unsupervised method, i.e. the class labels of the training data are not used when computing the NMF. However, incorporating the classification labels into the NMF algorithms allows to specifically guide them towards the extraction of data patterns relevant for discriminating the respective classes. This approach is particularly suited for the analysis of mass spectrometry imaging (MSI) data in clinical applications, such as tumor typing and classification, which are amongst the most challenging tasks in pathology. Thus, we investigate algorithms for extracting tumor specific spectral patterns from MSI data by NMF methods. Results: In this paper, we incorporate a priori class labels into the NMF cost functional by adding appropriate supervised penalty terms. Numerical experiments on a MALDI imaging dataset confirm that the novel supervised NMF methods lead to significantly better classification accuracy and stability as compared to other standard approaches.
关键词： MALDI imaging,tumor typing,Non-negative matrix factorization,mass spectrometry imaging,supervised learning
更新于2025-09-23 15:21:01
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Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis
摘要： Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and b-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively.
关键词： Baseline correction,Biochemical quantification,Hyperspectral image analysis,Multivariate curve resolution,Non-negative matrix factorization,Raman spectroscopy
更新于2025-09-10 09:29:36
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Hyperspectral Tissue Image Segmentation using Semi-Supervised NMF and Hierarchical Clustering
摘要： Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease-states can be directly assessed by analyzing the mid-IR spectra of different cell-types (e.g. epithelial cells) and sub-cellular components (e.g. nuclei), provided we can accurately classify the pixels belonging to these components. The challenge is to extract information from hundreds of noisy mid-IR bands at each pixel, where each band is not very informative in itself, making annotations of unstained tissue HSI images particularly tricky. Because the tissue structure is not necessarily identical between the two sections, only a few regions in unstained HSI image can be annotated with high confidence, even when serial (or adjacent) H&E stained section is used as a visual guide. In order to completely use both labeled and unlabeled pixels in training images, we have developed an HSI pixel classification method that uses semi-supervised learning for both spectral dimension reduction and hierarchical pixel clustering. Compared to supervised classifiers, the proposed method was able to account for the vast differences in spectra of sub-cellular components of the same cell-type and achieve an F1-score of 71.18% on two-fold cross-validation across 20 tissue images. To generate further interest in this promising modality we have released our source code and also showed that disease classification is straightforward after HSI image segmentation.
关键词： microspectroscopy,semi-supervised learning,hierarchical clustering,Hyperspectral imaging,non-negative matrix factorization
更新于2025-09-09 09:28:46
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[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Face Recognition Using Exact Gaussian-Hermit Moments
摘要： Face recognition systems have gained more attention during the last decades. Accurate features are the corner stones in these systems where the performance of recognition and classification processes mainly depends on these features. In this chapter, a new method is proposed for a highly accurate face recognition system. Exact Gaussian-Hermit moments (EGHMs) are used to extract the features of face images where the higher order EGHMs are able to capture the higher-order nonlinear features of these images. The rotation, scaling and translation invariants of EGHMs are used to overcome the geometric distortions. The non-negative matrix factorization (NMF) is a popular image representation method that is able to avoid the drawbacks of principle component analysis (PCA) and independent component analysis (ICA) methods and is able to maintain the image variations. The NMF is used to classify the extracted features. The proposed method is assessed using three face datasets, the ORL, Ncku and UMIST which have different characteristics. The experimental results illustrate the high accuracy of the proposed method against other methods.
关键词： Feature extraction,Face recognition,Exact Gaussian-Hermit moments,Non-negative matrix factorization,Classification
更新于2025-09-04 15:30:14