Comparative Analysis of Wavelet-Based Image Fusion Techniques for Remote Sensing Applications

This paper provides a comparative evaluation of image fusion methods, namely Wavelet Transform, Fixed Directional Wavelet Transform, and Intensity-Hue-Saturation (IHS) techniques, in the fusion of multispectral (MSI) and panchromatic (PAN) imagery. Image fusion is an essential process in remote sensing that combines spatial and spectral resolutions to enhance classification and interpretation in different applications such as land-use mapping, agricultural monitoring, and environmental analysis. The investigations compare the performance of these fusion methods in accordance with several important measures including spatial and spectral fidelity, computational efficiency, and classification accuracy. The paper discusses the strengths and weaknesses of each approach and their appropriateness for particular remote sensing applications. By critical assessment of the performance of Wavelet, Fixed Directional Wavelet, and IHS methods, this paper sheds light on the most suitable fusion technique to optimize image quality and usefulness. The results are expected to assist in informed decision-making in remote sensing by determining the best image fusion methods for improved data analysis and interpretation.