scholarly journals Relative Weighted Feature Space for Dimensionality Reduction and Classification of Hyperspectral Images

2017 ◽  
Author(s):  
Seyyed Ali Ahmadi ◽  
Nasser Mehrshad ◽  
Seyyed Mohammad Razavi
Keyword(s):  
Data Analysis ◽  
Dimension Reduction ◽  
Arbitrary Dimension ◽  
Feature Space ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Similarity Function ◽  
Data Set ◽  
Weighting Matrix ◽  
Spectral Bands

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

Dense Bathymetry in Turbid Coastal Zones Using Airborne Hyperspectral Images

2021 ◽  
Vol 87 (12) ◽  
pp. 923-927
Author(s):  
Steven Martinez Vargas ◽  
Claudio Delrieux ◽  
Katy L. Blanco ◽  
Alejandro Vitale
Keyword(s):  
Hyperspectral Images ◽  
Average Error ◽  
Support Vector ◽  
Buenos Aires Province ◽  
Coastal Zones ◽  
Bahía Blanca Estuary ◽  
Data Set ◽  
Spectral Bands ◽  
Unseen Data ◽  
Blanca Estuary

We used airborne hyperspectral images to generate a dense survey of bathymetric data in the Bahía Blanca estuary (Buenos Aires Province, Argentina). This estuarine area is characterized by intense sediment transport turning the water muddy, and thus optical bathymetric estimations are difficult. We used 24 spectral bands in a range of 500–900 nm acquired with a hyperspectral camera aboard an unmanned aerial vehicle, together with 100 bathymetry data points surveyed with a sonar sensor aboard an unmanned surface vehicle, covering an area of about 800 m2. Random-forest and support-vector-machine regressors were trained with this data set. The resulting model yielded a determination coefficient of 0.815 with unseen data, a root-mean-square error of 0.166 m, and an absolute average error less than 2%. These results allow dense and accurate reconstructions of the underwater profile in wide, muddy, shallow regions of the Bahía Blanca estuary, showing the feasibility of hyperspectral imagery combined with sonar data in turbid shallow waters.

scholarly journals Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Bo She ◽  
Fuqing Tian ◽  
Weige Liang ◽  
Gang Zhang
Keyword(s):  
Fault Detection ◽  
Dimension Reduction ◽  
Condition Monitoring ◽  
Data Structures ◽  
Principal Component ◽  
Feature Space ◽  
Kernel Principal Component Analysis ◽  
Local Data ◽  
Data Set ◽  
Global And Local

The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

scholarly journals Adaptive hierarchical clustering for hyperspectral image classification: Umbrella Clustering

2019 ◽  
Author(s):  
S.S.P. Vithana ◽  
E.M.M.B. Ekanayake ◽  
A.R.M.A.N. Rathnayake ◽  
G.C. Jayatilaka ◽  
H.M.V.R. Herath ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
Human Perception ◽  
Feature Space ◽  
Sri Lankan ◽  
Improve Performance ◽  
Data Set ◽  
Linear Discriminant ◽  
Spectral Bands ◽  
Relative Proximity

Hyperspectral Imaging (HSI) utilises the reflectance information of a large number of contiguous spectral bands to solve various problems. However, the relative proximity of spectral signatures among classes can be exploited to generate an adaptive hierarchical structure for HSI classification. This enables a level by level optimisation for clustering at each stage of the hierarchy. The Umbrella Clustering algorithm, introduced in this work, utilises this premise to significantly improve performance compared to non-hierarchical algorithms which attempt to optimise clustering globally. The key feature of the proposed methodology is that, unlike existing hierarchical algorithms which rely on fixed or supervised structures, the proposed method exploits a mechanism in spectral clustering to generate a self-organised hierarchy. The algorithm gradually zooms into the feature space to identify levels of clustering at each stage of the hierarchy. The results further demonstrate that the generated structure tallies with human perception. In addition, an improvement to Linear Discriminant Analysis (LDA) is also introduced to further improve performance. This modification maximises the pairwise class separation in the feature space. The entire algorithm includes this modified LDA step which requires a certain amount of class information in terms of features, at the training phase. The classification algorithm which incorporates all novel concepts was tested on the HSI data set of Pavia University as well the database of Common Sri Lankan Spices and Adulterants in order to assess the versatility of the algorithm.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

scholarly journals Multi-scale mesh saliency based on low-rank and sparse analysis in shape feature space

2015 ◽  
Vol 35-36 ◽  
pp. 206-214 ◽  
Author(s):  
Shengfa Wang ◽  
Nannan Li ◽  
Shuai Li ◽  
Zhongxuan Luo ◽  
Zhixun Su ◽  
...  
Keyword(s):  
Feature Space ◽  
Low Rank ◽  
Shape Feature ◽  
Mesh Saliency ◽  
Multi Scale

Twitter users exhibited coping behaviours during the COVID-19 lockdown: an analysis of tweets using mixed methods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruchi Mittal ◽  
Wasim Ahmed ◽  
Amit Mittal ◽  
Ishan Aggarwal
Keyword(s):  
Data Analysis ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Twitter Data ◽  
Long Run ◽  
The World ◽  
Twitter Users ◽  
The Government ◽  
Using Data

Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

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