量子电子学报 ›› 2019, Vol. 36 ›› Issue (6): 658-662.

• 光谱 • 上一篇    下一篇

一种面向对象的光谱分量替换变化检测方法

张爱明,秦慧杰,欧阳晓   

  1. 江苏省测绘工程院,江苏 南京 210013
  • 收稿日期:2019-01-28 修回日期:2019-04-08 出版日期:2019-11-28 发布日期:2019-11-19
  • 作者简介:张爱明(1974 -),江苏盐城人,学士,高级工程师,主要从事遥感应用方面的研究,E-mail: chgcyzam@126.com

An object oriented detection method for spectral component substitution change

ZHANG Aiming, QIN Huijie, OUYANG Xiao   

  1. Jiangsu Province Surveying and Mapping Engineering Institute,  Nanjing 210013, China
  • Received:2019-01-28 Revised:2019-04-08 Published:2019-11-28 Online:2019-11-19

摘要: 传统的基于光谱的遥感影像变化检测方法一般采用光谱的均值、亮度、标准差等特征进行差值运算,但这些特征具有一定程度上的相关性,导致变化检测结果出现很多虚检,而且阈值的选择对变化检测结果的影响也较大。传统利用光谱特征进行变化检测多是基于像素,检测结果会存在椒盐现象。针对这些问题,论文提出了一种面向对象的光谱分量替换变化检测方法,主要是利用两期影像的单波段光谱进行组合替换,从而突出变化信息,并采用分类的方法将变化信息提取出来,经实验,论文方法的变化检测精度可达到86%左右。该方法简单有效,可有效辅助土地利用变化监测、地理国情覆盖变化监测等生产项目。

关键词: 遥感;分量替换, 多尺度分割, 分类, 变化检测

Abstract: Traditional spectral-based remote sensing image change detection methods usually use spectral mean, brightness, standard deviation and other features for difference calculation, but these features have a certain degree of correlation, resulting in a lot of false detection of change detection results, and the selection of threshold has a greater impact on the change detection results. The traditional detection methods based on spectral features are mostly based on pixels, and the detection results will have salt and pepper phenomenon. In order to solve these problems, an object-oriented spectral component substitution change detection method is proposed in this paper, which mainly uses the single-band spectrum of two-phase images for combination substitution, so as to highlight the change information. The change information is extracted by classification method. The experiment shows that the change detection accuracy of this method can reach 90%. About%. This method is simple and effective, and can effectively assist land use change monitoring, geographical and national conditions cover change monitoring and other production projects.

Key words:  remote sensing; component substitution, multiscale segmentation; , classification, change detection