TY  - JOUR
T1  - Dimensionality Reduction of Remotely Sensed Hyperspectral Image for
Classification using PCA with Autoencoder Technique
AU - Shivakumar, B.R. AU - Prakash, J. 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 2
SP  - 57
EP  - 66
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.57.66
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.57.66
KW  - Auto encoder and principle component analysis
KW  -classification
KW  -convolutional neural network
KW  -deep
neural network
KW  -dimensionality reduction
KW  -hyperspectral image
AB  - 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.
ER  -