利用贝叶斯网络进行遥感变化检测

多时相遥感信息变化检测及其算法探索是当前国际遥感领域研究的热点,但是贝叶斯网络在遥感数据分类、特别是应用在变化检测方面的文献却很少。本文介绍了利用贝叶斯网络的变量间独立性测试原理,构建了输入两个时相多波段遥感信息的有向无环结构,利用训练后的网络进行两个时相多波段遥感变化信息的检测,取得了较好的效果。对北京六环线以内区域,1994年、2003年5种地类变化信息的遥感数据检测和类型转换进行了统计,其中耕地转换为城镇的占整个区域的26.52%,绿地增加占整个区域4.68%,水体减少占整个区域6.78%,导致裸地增加占整个区域4.80%,这个结果也在1∶5万的航空影像和地面上得到了验证。实验结果表明,贝叶斯网络为遥感数据的直接变化检测提供了一种新的途径。

Remote Sensing Change Detection Using Bayesian Networks

In recent years, the Bayesian network has been used in many study fields as a data-mining tool, but so far it is seldom used to process remote sensing data. In this paper, we introduce the algorithm about constructing Bayesian network classifier for remote sensing data based on the conditional mutual information test of different bands. The technical procedures of change detection with remote sensing data using Bayesian network are also presented, and the multi temporal Landsat TM data of Beijing acquired in 1994 and 2003 are taken as an example and performed with change detection. The change detection results show that from the year 1994 to 2003, 26.52% farmland of study area had been changed to urban land, 4.68% greenland was increased. The Directed Acyclic Graph (DAG) of Bayesian network describes the mutual information of multi-characteristic data, which synthesized the prior probability and sample information. The study results suggest that Bayesian network will be a newly effective approach for remote sensing data change detection.