Spectral-polarization imaging technology plays a crucial role in remote sensing detection, enhancing target identification and tracking capabilities by capturing both spectral and polarization information reflected from object surfaces. However, the acquisition of multi-dimensional data often leads to extensive datasets that necessitate comprehensive analysis, thereby impeding the convenience and efficiency of remote sensing detection. To address this challenge, we propose a fusion algorithm based on spectral-polarization characteristics, incorporating principal component analysis (PCA) and energy weighting. This algorithm effectively consolidates multi-dimensional features within the scene into a single image, enhancing object details and enriching edge features. The robustness and universality of our proposed algorithm are demonstrated through experimentally obtained datasets and verified with publicly available datasets. Additionally, to meet the requirements of remote sensing tracking, we meticulously designed a pseudo-color mapping scheme consistent with human vision. This scheme maps polarization degree to color saturation, polarization angle to hue, and the fused image to intensity, resulting in a visual display aligned with human visual perception. We also discuss the application of this technique in processing data generated by the Channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS). Experimental results demonstrate a significant enhancement in the information entropy and average gradient of the fused image compared to the optimal image before fusion, achieving maximum increases of 88% and 94%, respectively. This provides a solid foundation for target recognition and tracking in airborne remote sensing detection.