A Single Classifier Using Principal Components Vs Multi-Classifier System: In Landuse-LandCover Classification of WorldView-2 Sensor Data
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 f...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-11-01
|
| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-8/91/2014/isprsannals-II-8-91-2014.pdf |
| Summary: | 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. |
|---|---|
| ISSN: | 2194-9042 2194-9050 |