关键词: 微粒群优化, 多时相遥感影像, 变化检测

Abstract:

Aiming at the restriction of training samples distribution and limitation of feature combinations caused by traditional methods in the current multi-temporal remote sensing image direct change detection application, this paper introduces the particle swarm optimization（PSO）method 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 to 2006, 2006 to 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.

Key words: Particle Swarm optimization algorithm, multi-temporal remote sensing image, change detection