Enhancing Image Fusion via Optimized BAT Algorithm in Brovey transform for Remote Sensing Images

The goal of merging high spatial and multispectral data is to produce an image that retains both spatial information from the high-resolution panchromatic data and spectral attributes from the lower-resolution multispectral data. In this approach we provide a new approach by optimizing Brovey transform method by using the Bat Algorithm (BAT). The main goal is to improve the BT method by optimizing weights through the Loudness value (Ai range) and the rate of pulse emission (ri range), which will increase spectral information, reduce colour distortion, and improve spatial resolution. The optimized Brovey Transform fusion approach with $\mathrm{Ai}=2$ and $\mathrm{ri}=1$ greatly increases spatial resolution while successfully retaining spectrum contents, according to a qualitative evaluation using visual comparisons. The Bat Algorithm-optimized approach shows off its superiority in colour enhancement and spatial resolution, indicating its promise for applications that need improved visual discriminating. The Quantitative evaluation findings show that the suggested fusion approach performs exceptionally well in correlation (CC-0.643), peak signal-tonoise ratio (PSNR 19.415), and universal quality index (UQI0.931), although having a slightly larger RMSE (29.456). The fusion approach is noteworthy for achieving the highest values in both CC and UQI measurements, which indicate improved picture quality and greater correlation. The PSNR statistic, which shows better clarity and less noise, further confirms the superiority of the suggested methodology.