Hyperspectral image classification via principal component analysis, 2D spatial convolution, and support vector machines

2021 ◽  
Vol 15 (03) ◽  
Author(s):  
Guang Y. Chen ◽  
Wenfang Xie ◽  
Adam Krzyzak ◽  
Shen-En Qian
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.


2015 ◽  
Vol 12 (2) ◽  
pp. 349-353 ◽  
Author(s):  
Lianru Gao ◽  
Jun Li ◽  
Mahdi Khodadadzadeh ◽  
Antonio Plaza ◽  
Bing Zhang ◽  
...  

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