A Comparison of Supervised and Unsupervised Dimension Reduction Methods for Hyperspectral Image Classification

Hyperspectral images are extensively used, now-a-days, for governing several geo-spatial fields. Due to the high number of dimensions or spectral bands the classification accuracy demotes which is known as the “Hughes Phenomenon” or the “Curse of Dimensionality”. To overcome this obstacle dimensionality reduction approaches need to be performed or simply the number of the spectral bands needs to be reduced. As a preprocessing step feature extraction or feature selection can be performed that reduces the computational complexity of the hyperspectral data classification. Highly correlated features are omitted and only the informative ones are considered for the classification. In this paper, we considered the feature extraction approaches, namely, the Principal Component Analysis, the Linear Discriminant Analysis and both of them combined. We have applied these feature extraction methods on the dataset individually and then classified the dataset using Support Vector Machine classifier. The experimental results show that the LDA approach provides the best accuracy (86.53%) among the three dimension reduction techniques applied. All the files and codes used in our work can be found at https://github.com/joybiS31/multiclassSVM classification u sing PCA LDA/