Merging SPOT XS and SAR imagery using the wavelet transform method to improve classification accuracy

SPOT XS imagery is often used to generate land use categories. Foresters, urban planners, environmental engineers, environmental scientists, and other researchers then use the resulting land use categories. State of the knowledge research in remote sensing attempts to improve the accuracy of classification techniques. This study examines the effectiveness of the wavelet technique for the fusion of SAR (synthetic aperture radar) and SPOT XS imagery before classification. The wavelet technique least distorts the special characteristics of the SPOT XS imagery while combining with the SAR imagery. A maximum likelihood algorithm is then employed for the fused data and SPOT XS data alone. Comparison using the same accuracy assessment data reveals differences in classification. Complementary characteristics of the SPOT XS and SAR imagery improve the classification accuracy.