scholarly journals Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas

2009 ◽  
Vol 2009 (1) ◽  
Author(s):  
Mathieu Fauvel ◽  
Jocelyn Chanussot ◽  
Jón Atli Benediktsson
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Remote Sensing ◽  
Kernel Principal Component Analysis ◽  
Sensing Data

Laser-Welding Spots Detection Based on Original Moment Values

2014 ◽  
Vol 599-601 ◽  
pp. 974-980
Author(s):  
Xiao Long Qi ◽  
Bin Fang ◽  
Shu Mei Wang
Keyword(s):  
Principal Component Analysis ◽  
Laser Welding ◽  
Image Classification ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Invariant Moments ◽  
The Past

In the past decades, the theories of invariant moments have been researched extensively and wildly used in many fields. However, for the laser-welding spots of titanium tubes or other fixed objects, the invariant moments are inapplicable. Besides, the studies and experiments about image classification by means of the original moment values were barely proposed. In this paper, the method of classification based on original moment values is introduced, and an improved approach of KPCA (kernel principal component analysis) in order to reduce the inner-class distance of the qualified laser-welding spots is also discussed. Finally, experiments are carried out to validate the classification ability, and results show that the original moment values are suited as pattern features in classification of fixed objects.

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