微粒群优化方法的遥感影像变化检测研究

Remote Sensing Image Change Detection Using Particle Swarm Optimization Algorithm

将微粒群优化方法引入遥感信息变化检测领域,构建了基于微粒群优化方法的遥感信息变化检测方法。在变化监测的过程中,通过变化规则的自动搜索和建立,实现了遥感影像变化信息的一次性直接提取。应用微粒群优化方法对北京试验区2000—2006年、2006—2009年两个时间段的遥感影像进行土地覆盖类型的变化信息检测,并与决策树(C4.5和PART)、最大似然等方法的变化检测结果进行对比分析。结果表明,微粒群优化方法能够自动搜索变化规则,得到的变化规则比决策树方法更简单,并能够获得更高的检测精度。

The particle swarm optimization（PSQ） method is introduced to the field of remote sensing change detection, and propose a new change detection method based on PSO algorithm. In the processing of change detection, it automatically searches the change rules, so it can directly achieve the change information at one time. Selecting Beijing area as experimental area, this paper demonstrates the land cover change detection information extraction in Beijing area from 2000 2006, 2006--2009 using the new method. The PSO method is also compared with C4.5, PART, Maximum Likelihood methods. The results show that the PSO algorithm can search change rules automatically, and can achieve simpler rule than C4.5 and PART, can achieve high precision than the other three methods.