Multiple-point simulation-based method for extraction of objects with spatial structure from remotely sensed imagery

Classification of Combining Spectral information and Spatial information upon Multiple-point statistics (CCSSM) is a method for information extraction that introduces multiple-point simulation (MPS) to increase the classification accuracy of remotely sensed imagery data by incorporating structural information through a training image. This paper focuses on (1) applying CCSSM using a multigrid approach to a Satellite Pour l'Observation de la Terre (SPOT) 5 image, (2) adopting consensus-based fusion to combine two different information sources, the spectral information from supervised classification and spatial structure information from the MPS and (3) analysing the change trend for the accuracy of information extraction and optimizing the proportions in the combination of the two different information sources. We demonstrate that, even if the spectral information from the SPOT 5 image used in the classification results in better classification accuracy, with the introduction of spatial structure information from MPS the accuracy of the information extraction can still be increased significantly.