A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

Chen, Ying Nong; Hsieh, Cheng Ta; Wen, Ming; Han, Chin Chuan; Fan, Kuo-Chin

doi:10.3390/rs71114292

Cited by 6 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, FLE [24,25] and multple kernel learning were integrated to reduce the dimensions of features for classifying the HSI data. A brief review of FLE, kernelization, and multple kernel learning are introduced in the following before the proposed methods.…”

Section: Related Workmentioning

confidence: 99%

“…Matrix L in Equation (1) could be expressed as a Laplacian form. More details could be referred to [24,25].…”

Section: Feature Line Embedding (Fle)mentioning

confidence: 99%

“…In previous works, the idea of nearest feature line embedding (FLE) was successfully applied in reducing dimension on face recognition [24] and classifying HSI [25]. However, the abundant nonlinear structures and information could not be efficiently extracted using only the linear transformation and single kernel.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

Chen

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

In this study, a novel multple kernel FLE (MKFLE) based on general nearest feature line embedding (FLE) transformation is proposed and applied to classify hyperspectral image (HSI) in which the advantage of multple kernel learning is considered. The FLE has successfully shown its discriminative capability in many applications. However, since the conventional linear-based principle component analysis (PCA) pre-processing method in FLE cannot effectively extract the nonlinear information, the multiple kernel PCA (MKPCA) based on the proposed multple kernel method was proposed to alleviate this problem. The proposed MKFLE dimension reduction framework was performed through two stages. In the first multple kernel PCA (MKPCA) stage, the multple kernel learning method based on between-class distance and support vector machine (SVM) was used to find the kernel weights. Based on these weights, a new weighted kernel function was constructed in a linear combination of some valid kernels. In the second FLE stage, the FLE method, which can preserve the nonlinear manifold structure, was applied for supervised dimension reduction using the kernel obtained in the first stage. The effectiveness of the proposed MKFLE algorithm was measured by comparing with various previous state-of-the-art works on three benchmark data sets. According to the experimental results: the performance of the proposed MKFLE is better than the other methods, and got the accuracy of 83.58%, 91.61%, and 97.68% in Indian Pines, Pavia University, and Pavia City datasets, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Matrix L in Equation (1) could be expressed as a Laplacian form. More details could be referred to [24,25].…”

Section: Feature Line Embedding (Fle)mentioning

confidence: 99%

See 1 more Smart Citation

Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

Chen

2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are seven null entries in Table 3. Thus, we computed K in Equation (13) and the mass combinations in Equation (12), which gave the belief in Equation (8), and we computed the plausibility by its relationship with the belief in Equations (10) and (11) or directly from the mass combinations in Equation (9), as follows: The belief interval ranges from 0.7923 to 0.8121, i.e., the two classifiers coincidentally believe that the input curve is in PD with a minimum probability of 0.7923 and a maximum probability of 0.8121. Therefore, the crisp output is from the PD class for the example input that matches with its true class from the reference.…”

Section: Example Fuzzification Belief and Plausibility Calculation Rmentioning

confidence: 99%

“…Fuzzification is a process that assigns a degree of fuzziness to a crisp input that belongs to a certain membership function. Fuzzification and fuzzy algorithms have been effectively adopted in a wide range of studies [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

et al. 2017

View full text Add to dashboard Cite

Abstract:Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The effect of unwanted, or impure, pixels occurring in these data varies with their distribution in the image; the effect is greater if impurity pixels are included in a classifier's training set. Andrews' curves enable the interpreter to select outlier or impurity data that can be grouped into a new category for classification. This study overcomes the above-mentioned problem and illustrates the novelty of applying Andrews' plots to satellite image data, and proposes a robust method for classifying the plots that combines Dempster-Shafer theory with fuzzy set theory. In addition, we present an example, obtained from real satellite images, to demonstrate the application of the proposed classification method. The accuracy and robustness of the proposed method are investigated for different training set sizes and crop types, and are compared with the results of two traditional classification methods. We find that outlier data are easily eliminated by examining Andrews' curves and that the proposed method significantly outperforms traditional methods when considering the classification accuracy.

show abstract

Dimensionality reduction method based on multiple feature-space collaborative discriminative projection for rotor fault diagnosis

Dong¹,

Zhao²,

Yuan³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

At present, the trend of complex and intelligent rotating machinery and equipment is becoming more and more obvious, which generates a large amount of high-dimensional and nonlinear fault monitoring data that is difficult to handle. This makes the traditional dimensionality reduction algorithms based on point-to-point metrics or a small number of graph embedding structures lose their utility. To solve this problem, a multiple feature-spaces collaborative discriminative projection (MFSCDP) algorithm for rotor fault dataset dimensionality reduction is proposed. The algorithm first improves the projection metric from sample point to feature space into the median metric in order to achieve the effect of weakening the extrapolation error of the algorithm, and based on this, we propose a sample point-to-point guided nearest-neighbor feature space selection method to improve the construction efficiency of the feature space embedding graph. Then, by using Relief F to indirectly construct the reduced dimensional projection matrix with multiple feature spaces of collaboration. Finally, the proposed MFSCDP algorithm is used for the dimensionality reduction process of the rotor fault dataset. The algorithm's performance was verified using experimental information from rotor failure simulations of two different structural types. The result shows that the algorithm can reduce the difficulty of fault classification and improve the accuracy of identification.

show abstract

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

Cited by 6 publications

References 25 publications

Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification

A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning

Dimensionality reduction method based on multiple feature-space collaborative discriminative projection for rotor fault diagnosis

Contact Info

Product

Resources

About