Abstract:

In this paper, a Bayesian networkbased context model (RCBN) is presented to classify high resolution remote sensing (HRRS) images. First of all, image regions are classified by SVMs and candidate regions for each ground cover types are obtained. Then, the hybrid streams of candidate regions and spatial context information are piped into the context model, which will produce the category labels of regions by performing inference through the network. The RCBN consists of three kinds of nodes, which are nodes for candidate regions, related regions and contexts. The context nodes vary with different ground cover types, which are learned by the structure learning algorithm from training samples. Therefore, our RCBN model is capable of using the specific context information for each ground cover type, which makes the classification process more intelligent and efficient. The performance of the approach is evaluated qualitatively and quantitatively with comparative experiments, and the results show that the proposed methods are able to automatically classify and detect segments belonging to the same object classes.

Key words: high resolution remote sensing;image classification;context information;bayesian networks