Fusion of Graph Embedding and Sparse Representation for Feature Extraction and Classification of Hyperspectral Imagery

2017 ◽  
Vol 83 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Fulin Luo ◽  
Hong Huang ◽  
Jiamin Liu ◽  
Zezhong Ma
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Hyperspectral Imagery ◽  
Graph Embedding

scholarly journals Applying class-based feature extraction approaches for supervised classification of hyperspectral imagery

2007 ◽  
Vol 33 (3) ◽  
pp. 162-175 ◽  
Author(s):  
Xin Miao ◽  
Peng Gong ◽  
Ruiliang Pu ◽  
Raymond I Carruthers ◽  
Jill S Heaton
Keyword(s):  
Feature Extraction ◽  
Supervised Classification ◽  
Hyperspectral Imagery

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Sign in / Sign up

Export Citation Format

Share Document