Merging of IRS LISS III and PAN data—evaluation of various methods for a predominantly agricultural area

Abstract

The present study evaluates six different methods of data merging using Indian Remote Sensing Satellite (IRS) panchromatic (PAN; high spatial resolution) and Linear Imaging Self-scanning Sensor (LISS) III (high spectral resolution) data for a predominantly agricultural area including a potato research farm in Jalandhar, Punjab, India. The methods used were intensity–hue–saturation (IHS), principal component substitution (PCS), high pass filter (HPF), Brovey, synthetic variable ratio (SVR) and cubic spline wavelet (CSW) technique. The LISS III data, which were of the same date as the PAN data, were registered to the PAN data by identifying ground control points (GCPs) and using the cubic convolution resampling method. Merged data products were generated using the above-mentioned six techniques. The merged products were evaluated on three aspects, i.e. visually, statistically and by comparing classification accuracy. The study could help to rank the suitability of various merging methods for agricultural land-use applications. The HPF, SVR and CSW merging methods were more accurate than the commonly used IHS, PCS and Brovey methods. The PCS was found to be least accurate among all.