Pansharpening constitutes a category of data fusion techniques designed to enhance the spatial resolution of multispectral (MS) images by integrating spatial details from a high-resolution panchromatic (PAN) image. This process combines the high-spectral data of MS images with the rich spatial information of the PAN image, resulting in a pansharpened output ideal for more effective image analysis, such as object detection and environmental monitoring. Traditionally developed for satellite data, our paper introduces a novel pansharpening approach customized for the fusion of Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectrometry (EDS) data. The proposed method, grounded in Partial Least Squares regression with Discriminant Analysis (PLS-DA), significantly boosts the spatial resolution of EDS data while preserving spectral details. A key feature of this approach involves partitioning the PAN image into intensity bins and dynamically adapting this division in cases of overlapping compounds with similar average atomic numbers. We evaluate the method’s effectiveness using in-house EDS images obtained from both even and uneven sample surfaces. Comparative analysis against existing benchmarks and state-of-the-art pansharpening techniques demonstrates superior performance in both spectral and spatial quality indicators for our method.

全色锐化是一类数据融合技术，旨在通过集成高分辨率全色 (PAN) 图像的空间细节来增强多光谱 (MS) 图像的空间分辨率。该过程将 MS 图像的高光谱数据与 PAN 图像的丰富空间信息相结合，产生全色锐化输出，非常适合更有效的图像分析，例如物体检测和环境监测。我们的论文传统上是为卫星数据开发的，介绍了一种为扫描电子显微镜 (SEM) 和能量色散 X 射线光谱 (EDS) 数据融合而定制的新型全色锐化方法。所提出的方法以偏最小二乘判别分析回归 (PLS-DA) 为基础，显着提高了 EDS 数据的空间分辨率，同时保留了光谱细节。该方法的一个关键特征是将 PAN 图像划分为强度区间，并在具有相似平均原子序数的重叠化合物的情况下动态调整这种划分。我们使用从平坦和不平坦的样品表面获得的内部 EDS 图像来评估该方法的有效性。与现有基准和最先进的全色锐化技术的比较分析表明，我们的方法在光谱和空间质量指标方面均具有卓越的性能。