Subpixel Mapping of Multispectral Images Using Markov Random Field With Graph Cut Optimization

This letter presents a novel subpixel mapping method based on Markov random field (MRF) and graph cut optimization. First, the support vector regression is applied to generate the fractional images of classes based on randomly selected training pixels. Second, the spatially adaptive MRF-based subpixel mapping method is adopted to formulate the energy function. Third, to minimize the energy function, graph cut is applied. Finally, the subpixel vector border is extracted and integrated as a weighting function to fine-tune the smoothing parameters. We conduct the experiments on a downsampled SPOT-7 multispectral image and two synthetic images with regular and irregular objects, respectively. The results prove that graph cut can be an alternative method to the commonly used simulated annealing for the energy function optimization in MRF-based subpixel mapping, considering its much higher efficiency and comparable classification accuracy. Moreover, the integration of subpixel edge information is helpful to improve the accuracy of subpixel mapping results.