Abstract
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.
Highlights
Availability of more than one spectral channel in a satellite remote sensing dataset enables us to study and analyse various natural and artificial phenomena by extracting information through image analysis techniques
Though Principal Component Analysis (PCA) helps in reducing computational demands and avoids the need for larger number of representative samples, it should be noted that lower order components or components with small variance do have some discriminating information leading to loss of information (Geiger and Kubin, 2012)
We investigate the utility of multi-classifier systems (MCS) in LULC classification of WorldView-2 sensor data without the need to perform dimensionality reduction
Summary
Availability of more than one spectral channel in a satellite remote sensing dataset enables us to study and analyse various natural and artificial phenomena by extracting information through image analysis techniques. There is redundant information found in more than one spectral channel which further adds to unnecessary computational demand In such cases dimensionality reduction methods are employed to resolve the issues. Principal Component Analysis (PCA) is widely used as dimensionality reduction technique in literature (Jolliffe, 2005, Gonzalez and Woods, 2002). It condenses most of the information spread across many channels into fewer number of channels. Though PCA helps in reducing computational demands and avoids the need for larger number of representative samples, it should be noted that lower order components or components with small variance do have some discriminating information leading to loss of information (Geiger and Kubin, 2012)
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