Classification is a crucial task in various remote sensing applications. While edge is one of the most important characteristics in the high-resolution remote-sensing images, which helps much for the improvement of classification accuracy. Therefore, in this paper, we propose an unsupervised classification method by incorporating edge information into a clustering procedure. Firstly, a consistency coefficient function, which indicates the similarity between edges obtained by clustering and by the edge detection methods, is defined to guarantee more accurate edges. Sequentially, a clustering procedure based on HMRFFCM is designed, in which the edge constraints are exploited by using the edge consistency. Experiments on synthetic and real remote sensing images have shown that the proposed methods can get more accurate classification results.
It is rather difficult for low-level visual features to describe the need for specific applications of image understanding, which results in the inconsistency between vision information and application need. In this paper, a new model is proposed to reduce this gap by combining low-level visual features with semantic features. It uses the output of neural network as the semantic feature, which is accompanied with the priori label features to describe the image after making normalization. And then, the proposed method employs Potts to model the distribution of label priori, and utilizes the Bayesian network to classify images. Several experiments on both synthetic and real images have verified that this method can get more accurate segmentation.