Fusion of multisensor remote sensing data: Assessing the quality of resulting images

The primary attention of this study was to examine what improvement can be obtained for classification accuracies by using different merging techniques done with multisensor dataset. In this study, the existing fusion techniques that preserve spectral characteristics, while increase spatial characteristic such as Principle Component Analysis, Intensity-Hue-Saturation, Brovey and Multiplicative algorithms were applied to multi sensor data set. IRS 1 D Pan, LISS III and ERS images were used. Using fusion techniques IRS 1 D imagery combined with LISS III data and ERS radar data combined with LISS III remotely sensed data. Maximum Likelihood classification algorithm was applied to classify fused imageries. Before classification procedure training sites were selected for all various land cover/use categories. Classification accuracy assessment was calculated using an error matrix for all images. Finally, the results of classification accuracy were compared and the best result was obtained by combining IRS 1 D image with LISS III data by means of IHS colour transformation technique.