A Survey on Image Fusion Techniques and Performance Metrics

Image fusion is the process of merging two or more relevant information into one image. The resulted image will have more explanatory than original images. Multispectral image (MS) is obtained from satellite, multispectral image having rich spectral information and low spatial resolution. MS have less information which is not suitable for remote sensor application. Panchromatic (PAN) image is one of the types of satellite images. PAN images have more spectral information but low spatial information. In remote sensing application more spatial and spectral information is required, so merging MS and PAN will result in rich spatial and spectral image. Many fusion algorithms are supported to fuse MS and PAN. Some techniques are principal component analysis, discrete wavelet transform, pixel-level image fusion and multisensor image fusion. Qualitative analysis determines the performance of fused image by comparison between original image and resulted fused image. Some qualitative metrics are evaluated using Root mean square error (RMSE), Relative global dimensional synthesis error (ERGAS), Quality factor (Q4), Cross correlation (CC) and Spectral angle mapper (SAM). This paper reviews about various fusion techniques in remote sensor and quality metrics.