Performance evaluation of FFT_PCA Method based on dimensionality reduction algorithms in improving classification accuracy of OLI data

Fusions of panchromatic and multispectral images create new permission to gain spatial and spectral information together. This paper focused on hybrid image fusion method FFT-PCA, to fuse OLI bands to apply Dimensionality Reduction (DR) methods (PCA, ICA and MNF) on this fused image to evaluate the effect of these methods on final classification accuracy. A window of OLI images from Ardabil County was selected to this purpose and preprocessing method like atmospheric and radiometric correction was applied on this image. Then panchromatic (band8) and multispectral bands of OLI were fused with FFT-PCA method. Three dimensionality reduction algorithms were applied on this fused image and the training data for classification were selected from DRs Output. A total of eight classes include bare land, rich range land, water bodies, settlement, snow, agricultural land, fallow and poor range land were selected and classified with support vector machine algorithm. The results showed that classification based on dimensionality reduction algorithms was quite good on OLI data classification. Overall accuracy and kappa coefficient of classification images showed that ICA, PCA and MNF methods 86.9%, 89%, 96.8% and 0.84, 0.91, 0.96 respectively. The MNF based image classification has higher classification accuracy between two others. PCA and ICA have lower accuracy than MNF respectively.