New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study

This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications.