Remote Sensing Image Fusion Approach Based on Brovey and Wavelets Transforms

This paper proposes a remote sensing image fusion approach based on a modified version of Brovey transform and wavelets. The aim is to reduce the spectral distortion in the Brovey transform and spatial distortion in the wavelets transform. The remote sensing data sets has been chosen for the image fusion process and the data sets were selected from different satellite images in south western Sinai, Egypt. Experiments were conducted on a variety of images, and the results of the proposed image fusion approach were compared with principle component analysis and the traditional Brovey approach. The obtained results show that the proposed approach achieves less deflection and reduces the distortion. Several quality evaluation metrics were used for the proposed image fusion like standard deviation, correlation coefficient, entropy information, peak signal to noise ratio, root mean square error and structural similarity index. Experimental results obtained from proposed image fusion approach prove that the use of the Brovey with wavelets can efficiently preserve the spectral information while improving the spatial resolution of the remote sensing.