An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery

Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability (δ) in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and δ , which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).

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Randomized independent component analysis and linear discriminant analysis dimensionality reduction methods for hyperspectral image classification

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.14.036507 ◽

2020 ◽

Vol 14 (03) ◽

pp. 1

Author(s):

Chippy Jayaprakash ◽

Bharath Bhushan Damodaran ◽

Sowmya Viswanathan ◽

Kutti Padannayil Soman

Keyword(s):

Discriminant Analysis ◽

Independent Component Analysis ◽

Dimensionality Reduction ◽

Linear Discriminant Analysis ◽

Image Classification ◽

Hyperspectral Image ◽

Independent Component ◽

Hyperspectral Image Classification ◽

Linear Discriminant ◽

Reduction Methods

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Comparative study of different dimensionality reduction methods in hyperspectral image classification

Journal of Physics Conference Series ◽

10.1088/1742-6596/2024/1/012009 ◽

2021 ◽

Vol 2024 (1) ◽

pp. 012009

Author(s):

Lei Kang ◽

Xiaoqing Hu ◽

Chengcheng Zhong ◽

Kai Zhang ◽

Yanan Jiang

Keyword(s):

Comparative Study ◽

Dimensionality Reduction ◽

Image Classification ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Reduction Methods

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Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Advances in Space Research ◽

10.1016/j.asr.2013.11.027 ◽

2014 ◽

Vol 53 (12) ◽

pp. 1720-1734 ◽

Cited By ~ 11

Author(s):

Bharath Bhushan Damodaran ◽

Rama Rao Nidamanuri

Keyword(s):

Dimensionality Reduction ◽

Image Classification ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Multiple Classifier System ◽

Classifier System ◽

Multiple Classifier ◽

Reduction Methods ◽

The Impact

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Speech Emotion Recognition Based on Sparse Representation

Archives of Acoustics ◽

10.2478/aoa-2013-0055 ◽

2013 ◽

Vol 38 (4) ◽

pp. 465-470 ◽

Cited By ~ 11

Author(s):

Jingjie Yan ◽

Xiaolan Wang ◽

Weiyi Gu ◽

LiLi Ma

Keyword(s):

Dimensionality Reduction ◽

Emotion Recognition ◽

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Speech Emotion Recognition ◽

Least Squares Regression ◽

Computer Science Pedagogy ◽

Reduction Methods ◽

Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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Supervised dimensionality reduction for big data

Nature Communications ◽

10.1038/s41467-021-23102-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joshua T. Vogelstein ◽

Eric W. Bridgeford ◽

Minh Tang ◽

Da Zheng ◽

Christopher Douville ◽

...

Keyword(s):

Dimensionality Reduction ◽

Data Science ◽

Real Data ◽

Low Rank ◽

Conditional Moment ◽

Desktop Computer ◽

Reduction Techniques ◽

Reduction Methods ◽

The Individual ◽

Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

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