Abstract: The degree of polarization (DoP) and the angle of polarization (AoP) are crucial parameters for characterizing polarization properties. Polarization degree images are typically fused with intensity images to address the low brightness limitation inherent in DoP image. An experimental investigation into polarization characteristics across multiple observation angles and material types is designed in this paper. Utilizing six different white materials under spray-painted interference conditions, we systematically examine the variation patterns of DoP and polarization angle across different detection angles, and ultimately identify optimal observation angles for similar target recognition under various conditions. By implementing a multi-scale adaptive modulation fusion enhancement algorithm proposed in this research, we achieve effective fusion enhancement between intensity images and DoP images, significantly improving the discriminability of similar targets. Experimental results demonstrate significant improvements in fused images generated by our algorithm compared to wavelet transform-based fusion methods. The average information entropy is increased by 22.32%, standard deviation is improved by 44.50%, mean gradient is enhanced by 2.04 times, and inter-target discriminability is boosted by 15.78 times. The proposed algorithm facilitates the identification of similar targets, providing new perspectives and methodologies for the application of polarization detection technology in target recognition scenarios.