A Novel MRF-Based Multifeature Fusion for Classification of Remote Sensing Images

The spatial information has been proved to be effective in improving the performance of spectral-based classification. However, it is difficult to describe different image scenes by using monofeature owing to complexity of the geospatial scenes. In this letter, a novel framework is developed to combine the multiple spectral and spatial features based on the Markov random field (MRF). Specifically, the pixels in an image are separated into reliable and unreliable ones according to the decision of multifeature classifications. The labels of the reliable pixels can be conveniently determined, but the unreliable pixels are then classified by fusing the multifeature classification results and reducing the classification uncertainties based on the MRF optimization. Experiments are conducted on three multispectral high-resolution images to verify the effectiveness of the proposed method. Several state-of-the-art multifeature classification methods are also achieved for the purpose of comparison. Moreover, three classifiers (i.e., multinomial logistic regression, support vector machines, and random forest) are used to test the performance of the proposed framework. It is shown that the proposed method can effectively integrate multiple features, yield promising results, and outperform other approaches compared.