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Target-to-Background Separation for spectral unmixing in In-Vivo Fluorescence Imaging

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Journal of Shanghai Jiaotong university(Science) 2014年第5期19卷 600-611页

作者：赵勇胡程彭金良秦斌杰 School of Biomedical Engineering Shanghai Jiaotong University

We present a novel fluorescence spectral unmixing based on target-to-background separation preprocessing, which effectively separates the multi-target fluorescence from all background autofluorescence(BF)without any hardware-based BF acquisition and tissue specific BF estimation. Specifically, we first enhance the intrinsic accumulation contrast in target-to-background fluorescence using h-dome transformation; then separate multi-target fluorescence areas from the background in sparse multispectral data utilizing kernel maximum autocorrelation factor analysis; we further use fast marching-based image inpainting method to patch up the removed target fluorescence areas and reconstruct the multispectral BF; with the BF matrix being subtracted from the original data, the multi-target fluorophores are easily unmixed from the subtracted data using multivariate curve resolution-alternating least squares method. In two preliminary in-vivo experiments, the proposed method demonstrated excellent performance to unmix multi-target fluorescences while other state-of-art unmixing methods failed to get desired results.

关键词： fluorescence imaging spectral unmixing autofluorescence removal target detection kernel maximum autocorrelation factor target-to-background separation

FRET spectral unmixing:A Ratiometric Fluorescent Nanoprobe for Hypochlorite

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FRET Spectral Unmixing:A Ratiometric Fluorescent Nanoprobe f...

A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

第八届全国化学生物学学术会议

作者： Gengwen Chen Xiaojun Peng Fengling Song State Key Laboratory of Fine Chemicals Dalian University of Technology

＜正＞In the field of biological research and technology,Forster resonance energy transfer （FRET） has been a popular tool,1 whether in vitro or in *** metric fluorescent probes based on FRET allow the measurement of emission intensities at two different wavelengths,which should provide a built-in correction for environmental effect and can also increase the dynamic range of fluorescence ***,an undesirable overlap between these two emissions often complicates the determination of FRET efficiencies and cause misleading *** linear unmixing algorithms are available for correcting experiments’ results caused by signals bleed-through,the crosstalk of the FRET pairs’ emissions still makes FRET ratio metric approach not *** we describe a new construction method for FRET probe by loading two fluorophores on a hydrid nanoparticle to realize a true spectral unmixing and exhibit its chemo-sensing properties for hypochlorite. We proposed a new spectral unmixing approach by developing a new ratiometric fluorescence nanoprobe for hypochlorite based on the chemo sensing response of an aminocyanine dye towards ROS.4 The nanoprobe exhibited a ratiometric fluorescence response to hypochlorite by determination the value of 1575/*** we know,such an unprecedented big separation of FRET pairs’ emissions has not been reported before.

关键词： FRET Nanoprobe Hypochrite spectral unmixing Cyanine dye

来源： cnki会议评论

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Intelligent Automation & Soft Computing 2023年第8期37卷 2459-2476页

作者： M.R.Vimala Devi S.Kalaivani School of Electronics Engineering Vellore Institute of TechnologyVellore632051India

spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral *** spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental ***,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance ***,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral *** divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize *** endmember with the minimum error is chosen as the final endmember in each specific *** proposed method is simple and automatically considers endmember variability in hyperspectral *** efficiency of the proposed method is evaluated using two real hyperspectral *** average spectral angle and abundance angle are used to analyze the performance measures.

关键词： Hyperspectral image spectral unmixing spectral matching endmember bundles fuzzy inference system

Assessing tree crown fire damage integrating linear spectral mixture analysis and supervised machine learning on Sentinel-2 imagery

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International Journal of Digital Earth 2023年第1期16卷 3162-3198页

作者： Giandomenico De Luca Giuseppe Modica Joao M.N.Silva Salvatore Praticò JoséM.C.Pereira Dipartimento di Agrari1 Universitàdegli Studi Mediterranea di Reggio Calabri1Reggio Calabri1Italy Dipartimento di Scienze veterinarie Universitàdegli Studi di Messin1Messin1Italy Forest Research Centre School of AgricultureUniversity of LisbonLisbonPortugal

Crown fire damage is a mixture of three principal fire-related components:charred material,scorched foliage,and unaltered green *** study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts(Portugal and Italy)by applying linear spectral mixture analysis(LSMA)on Sentinel-2 *** tree crowns fire damage was subsequently mapped,integrating fractional abundance information in a random forest(RF)algorithm,comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative *** image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification(LMSAderived RMSE<0.1),the F-scores always were≥90%whether generic endmembers or image endmembers derived information was *** environmental heterogeneity of the two study areas affected the fire severity gradients,with a prevalence of the charred(PT)(45–46%)and green class(IT)(44–53%).Post-fire temporal monitoring was initialized by applying the proposed strategies,and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event,with a reduced charcoal predominance and an increasing proportion of green components.

关键词： Post-fire assessment fire severity post-fire vegetation recovery random forest(RF) scikit-learn fraction image extraction spectral unmixing endmembers crown fire damage mapping fully constrained least squares pixel purity index

Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing

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High Technology Letters 2012年第4期18卷 333-342页

作者：于钺 SunWeidong Department of Electronic Engineering Tsinghua University Beijing 100084 P. R. China China Ship Research and Development Academy Beijing 100192 P. R. China

This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.

关键词： hyperspectral data nonnegative matrix factorization (NMF) spectral unmixing convex function projected gradient (PG)

Recent advances in hyperspectral image processing

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Geo-Spatial Information Science 2012年第3期15卷 143-156页

作者： ZHANG Liangpei DU Bo State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan University129 Luoyu RoadWuhan 430079China School of Computer Science Wuhan University129 Luoyu RoadWuhan 430079China

Hyperspectral images(HSI)provide a new way to exploit the internal physical composition of the land *** basic platform for acquiring HSI data-sets are airborne or spaceborne spectral *** useful information from hyperspectral images can be grouped into four categories.(1)Classification:Hyperspectral images provide so much spectral and spatial information that remotely sensed image classification has become a complex task.(2)Endmember extraction and spectral unmixing:Among images,only HSI have a complete model to represent the internal structure of each pixel where the endmembers are the *** of endmembers from HSI thus becomes the foremost step in interpretation of each *** proper endmembers,the corresponding abundances can also be exactly calculated.(3)Target detection:Another practical problem is how to determine the existence of certain resolved or full pixel objects from a complex *** a reliable rule for separating target signals from all the other background signals,even in the case of low target occurrence and high spectral variation,comprises the key to this problem.(4)Change detection:Although change detection is not a new problem,detecting changes from hyperspectral images has brought new challenges,since the spectral bands are so many,accurate band-to-band correspondences and minor changes in subclass land objects can be depicted in *** this paper,the basic theory and the most canonical works are discussed,along with the most recent advances in each aspect of hyperspectral image processing.

关键词： hyperspectral images classification spectral unmixing endmembers extraction target detection hyperspectral change detection

Speed-up for N-FINDR algorithm

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Journal of Harbin Institute of Technology(New Series) 2008年第1期15卷 141-144页

作者：王立国张晔 Dept.of Information Engineering Harbin Institute of Technology Dept.of Information Engineering Harbin Institute of Technology

N-FINDR is a very popular algorithm of endmember (EM) extraction for its automated property and high efficiency. Unfortunately, innumerable volume calculation, initial random selection of EMs and blind searching for EMs lead to low speed of the algorithm and limit the applications of the algorithm. So in this paper two measures are proposed to speed up the algorithm. One of the measures is substituting distance calculation for volume calculation. Thus the avoidance of volume calculation greatly decreases the computational cost. The other measure is resorting dataset in terms of pixel purity likelihood based on pixel purity index (PPI) concept. Then, initial EMs can be selected well-founded and a fast searching for EMs is achieved. Numerical experiments show that the two measures speed up the original algorithm hundreds of times as the number of EMs is more than ten.

关键词： endmember extraction N-FINDR algorithm PPI algorithm spectral unmixing

Hypergraph Regularized Deep Autoencoder for Unsupervised unmixing Hyperspectral Images

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Journal of Donghua University(English Edition) 2023年第1期40卷 8-17页

作者：张泽兴杨斌 School of Computer Science and Technology Donghua UniversityShanghai 201620China

Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent *** a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data ***,most existing AE-based unmixing algorithms usually ignore the spatial information of *** solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for ***,the traditional AE architecture is specifically improved as an unsupervised unmixing ***,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their ***,L_(1/2)norm is further used to enhance abundances ***,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.

关键词： hyperspectral image(HSI) spectral unmixing deep autoencoder(AE) hypergraph learning