Authors : B.R. Shivakumar and J. Prakash
Abstract: Hyperspectral Imagery (HSI) is widely used in the application domains such as agriculture, environment, forestry and geology for the identification and observations which demands the efficient classification accuracy. The supervised classification is a challenging task due to limited number of available training samples compared to large number of spectral bands. This phenomena reduces the classification accuracy. To overcome this problem, the dimensionality reduction preprocessing step is adopted. This process reduces the number of spectral bands which leads to decrease in computational complexity and enhancement in classification accuracy. In this study, AEPCA (Auto Encoder and Principle Component Analysis) method is proposed for dimensionality reduction of HSI. The performance of AEPCA is evaluated against AE (Autoencoder) and PCA (Principle Component Analysis) method. The dimensionally reduced components are classified using CNN (Convolutional Neural Network) based classifier. The proposed model of dimensionality reduction demonstrates superior classification accuracy due to effective combination of characteristics of AE and PCA. The noisy or corrupted pixels are recovered by AE Model and high dimensional image is represented by efficient fewer number of principle components by PCA is the potential advantage of AEPCA Model.
B.R. Shivakumar and J. Prakash, 2019. Dimensionality Reduction of Remotely Sensed Hyperspectral Image for Classification using PCA with Autoencoder Technique. Asian Journal of Information Technology, 18: 57-66.