scholarly journals Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields

2016 ◽  
Vol 8 (4) ◽  
pp. 355 ◽  
Author(s):  
Haoyang Yu ◽  
Lianru Gao ◽  
Jun Li ◽  
Shan Li ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Random Fields ◽  
Markov Random Fields ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Markov Random ◽  
Vector Machines

Subspace-Based Support Vector Machines for Hyperspectral Image Classification

2015 ◽  
Vol 12 (2) ◽  
pp. 349-353 ◽  
Author(s):  
Lianru Gao ◽  
Jun Li ◽  
Mahdi Khodadadzadeh ◽  
Antonio Plaza ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Vector Machines

scholarly journals Hyperspectral image classification using support vector machines

2020 ◽  
Vol 9 (4) ◽  
pp. 684
Author(s):  
Jonnadula Dr.J.Harikiran Harikiran
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Bidimensional Empirical Mode Decomposition

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.

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