利用贝叶斯网络融合空间上下文的高分辨遥感图像分类

针对高分辨遥感图像,本文提出了一种基于贝叶斯网络的上下文模型,以及基于该模型的面向对象的遥感图像分类方法.首先,利用支持向量机(SVM)实现分割区域的初始分类,获得各个类别的候选区域.然后,利用提出的上下文模型融合候选区域及其周围区域的上下文信息,通过贝叶斯网络推理,将候选区域分类到各类地物类型中.基于贝叶斯网络的上下...

Integrating Contexts into the Classification of High-Resolution Remote Sensing Images Using the Bayesian Networks

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.