<p>Remote sensing technology plays a pivotal role in acquiring surface information, with multispectral (MS) and panchromatic (PAN) images being crucial data sources. However, the significant difference in spatial resolution between MS and PAN images poses challenges for fusion, often leading to image distortion. To address this, we propose an innovative adaptive multiscale decomposition fusion method. This approach leverages a structural attenuation (SA) index to guide the decomposition of PAN images, ensuring that the resulting low-frequency component possesses a spatial structure similar to the MS image’s spatial component. By fusing these components, we effectively eliminate distortion caused by spatial structure mismatch. The proposed method involves decomposing the MS image into spectral and spatial components using Lab color space transformation. The SA index, based on mean brightness, contrast, and spatial activity level, guides the adaptive multiscale decomposition of the PAN image. Experimental results on both simulated and real-world datasets demonstrate that our algorithm significantly enhances both spatial detail preservation and spectral fidelity in the fused images. Quantitative evaluations reveal superior performance compared to state-of-the-art methods, with notable improvements in spatial detail preservation and spectral fidelity. This research not only advances the field of remote sensing image fusion but also provides a valuable tool for improving the interpretive accuracy and application effectiveness of remote sensing imagery. The implementation is available at: <a href="https://github.com/Wohaizainuli/An-Adaptive-Multi-Scale-Decomposition-Fusion-Method">https://github.com/Wohaizainuli/An-Adaptive-Multi-Scale-Decomposition-Fusion-Method</a>.</p>