List of Articles FFT-PCA

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  1 - Performance evaluation of FFT_PCA Method based on dimensionality reduction algorithms in improving classification accuracy of OLI data
  Parviz Zeaiean Firooz Abadi1 Hasan Hasani Moghaddamb
  Fusions of panchromatic and multispectral images create new permission to gainspatial and spectral information together. This paper focused on hybrid image fusionmethod FFT-PCA, to fuse OLI bands to apply Dimensionality Reduction (DR)methods (PCA, ICA and MNF) on this f More

  Fusions of panchromatic and multispectral images create new permission to gainspatial and spectral information together. This paper focused on hybrid image fusionmethod 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 thesemethods on final classification accuracy. A window of OLI images from ArdabilCounty was selected to this purpose and preprocessing method like atmospheric andradiometric correction was applied on this image. Then panchromatic (band8) andmultispectral bands of OLI were fused with FFT-PCA method. Three dimensionalityreduction algorithms were applied on this fused image and the training data forclassification were selected from DRs Output. A total of eight classes include bareland, rich range land, water bodies, settlement, snow, agricultural land, fallow andpoor range land were selected and classified with support vector machine algorithm.The results showed that classification based on dimensionality reduction algorithmswas quite good on OLI data classification. Overall accuracy and kappa coefficient ofclassification 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 higherclassification accuracy between two others. PCA and ICA have lower accuracy thanMNF respectively. Manuscript profile
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  2 - FFT-PCA Image Fusion Based Flora and Vegetation Mapping Of Eshkevarat No Hunting Zone
  Zeinab Hoseinnejad Hasan Hasani Moghaddam Zahra Parvar Kourosh Kavousi Hamid Gashtasb Meigooni
  fusion of remote sensing data is essential in order to obtain more information from different images. Mapping the vegetation of an area is very important due to its environmental importance. In this research, used Landsat ETM+ images and field surveying to identify vege More

  fusion of remote sensing data is essential in order to obtain more information from different images. Mapping the vegetation of an area is very important due to its environmental importance. In this research, used Landsat ETM+ images and field surveying to identify vegetation states of the Eshkevarat No hunting zone. After applying necessary preprocessing like gap filling and atmospheric correction, the panchromatic and multi-spectral images were fused based on the FFT-PCA algorithm. In the next section, the fused image was classified based on the Support Vector Machine (SVM), algorithm into five classes. The results showed that the overall accuracy and kappa coefficient of classified images is 0.943% and 0.910 respectively. In order to field surveying of study area, 1-meter plots in 500-meter distance choose and 14 Flora and vegetation species were identified and mapped. The results showed that satellite images have good accuracy in this field but based on its spatial resolution limitations a large number of species present in the area have not been identified. In this research, it is suggested to use a combination of both satellite image sources and field surveys. Manuscript profile