Abstract
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 benefits offered by a combination of different 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 different types of image data, it is beneficial 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 unified representation that incorporates the potential strengths of a sensor in order to minimize classification 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.
