Study and Comparison of Remote Sensing Images Fusion Techniques

Data fusion is a formal framework in which is expressed means and tools for the alliance of data originating from different sources. The aim of image fusion techniques is to inject the spatial detail into the multispectral (MS) imagery while keeping the original spectral values. The developed modules, including six typical pan-sharpening algorithms, show that the framework can be applied to implement most algorithms. In this research, the multispectral and panchromatic images have been taken from Landsat 8 and SPOT. This paper contributes to the comparative evaluation of fused data for understand-ing the performance of implemented image fusion algorithms such as IHS (Intensity-Hue-Saturation), HPF (High Pass Frequency), Brovey, Multiplica- tion, SFIM (Smoothing Filter-based Intensity Modulation) and PCA (Principal Component Analysis) techniques. For the quality assessment; mean, standard deviation, average gradient, information entropy, and the correlation coeffi-cient methods were applied on the fused images. The result reveals that all the six methods have spectral distortion, Brovey and SFIM are the best in retaining spectral information of original images, but the PCA is the worst. The study reveals that all the fused images have higher spatial frequency information than the original images, and Brovey is the best method in retaining spectral in-formation.

Study and Comparison of Remote Sensing Images Fusion Techniques Keywords:

Image fusion , multispectral , panchromatic , pan-sharpening