A novel relearning approach for remote sensing image classification post-processing

In this paper, we proposed a relearning method for classification post-processing (CPP). CPP can be viewed as a label refinement method to improve the classification accuracy. The proposed approach considers the frequency and spatial arrangements of the labels to enhance the classification performance by iteratively learning the classification map. Experiments conducted on a series of images obtained by different sensors show that, the proposed relearning approach present promising performances compared to the state-of-the-art techniques such as filtering, Markov random field (MRF) and object-based voting.