关键词：卫星图像；数据融合；聚类；评价
摘 要：Satellite image data fusion is a topic of interest in many areas including environmental monitoring, emergency response, and defense. Typically any single satellite sensor cannot provide all of the bene ts o ered by a combination of di erent sensors (e.g., high-spatial but low spectral resolution vs. low-spatial but high spectral,optical vs. SAR). Given the respective strengths and weaknesses of the di erent types of image data, it is bene cial to fuse many types of image data to extract as much information as possible from the data.Our work focuses on the fusion of multi-sensor image data into a uni ed representation that incorporates the potential strengths of a sensor in order to minimize classi cation error. Of particular interest is the fusion of optical and synthetic aperture radar (SAR) images into a single, multispectral image of the best possible spatial resolution. We explore various methods to optimally fuse these images and evaluate the quality of the image fusion by using K-means clustering to categorize regions in the fused images and comparing the accuracies of the resulting categorization maps.