Volume II-8
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-8, 91-95, 2014
https://doi.org/10.5194/isprsannals-II-8-91-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-8, 91-95, 2014
https://doi.org/10.5194/isprsannals-II-8-91-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  27 Nov 2014

27 Nov 2014

A Single Classifier Using Principal Components Vs Multi-Classifier System: In Landuse-LandCover Classification of WorldView-2 Sensor Data

L .N. Eeti, K. M. Buddhiraju, and A. Bhattacharya L .N. Eeti et al.
  • Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India

Keywords: Loss in PCA, Multi-Classifier System, High Spectral-dimension, WorldView-2 image, principal components

Abstract. In remote sensing community, Principal Component Analysis (PCA) is widely utilized for dimensionality reduction in order to deal with high spectral-dimension data. However, dimensionality reduction through PCA results in loss of some spectral information. Analysis of an Earth-scene, based on first few principal component bands/channels, introduces error in classification, particularly since the dimensionality reduction in PCA does not consider accuracy of classification as a requirement. The present research work explores a different approach called Multi-Classifier System (MCS)/Ensemble classification to analyse high spectral-dimension satellite remote sensing data of WorldView-2 sensor. It examines the utility of MCS in landuse-landcover (LULC) classification without compromising any channel i.e. avoiding loss of information by utilizing all of the available spectral channels. It also presents a comparative study of classification results obtained by using only principal components by a single classifier and using all the original spectral channels in MCS. Comparative study of the classification results in the present work, demonstrates that utilizing all channels in MCS of five Artificial Neural Network classifiers outperforms a single Artificial Neural Network classifier that uses only first three principal components for classification process.