张春森, 李国君, 崔卫红. 一种基于矢量数据的遥感影像变化检测方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 309-317. DOI: 10.13203/j.whugis20190131
引用本文: 张春森, 李国君, 崔卫红. 一种基于矢量数据的遥感影像变化检测方法[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 309-317. DOI: 10.13203/j.whugis20190131
ZHANG Chunsen, LI Guojun, CUI Weihong. A Change Detection Method for Remote Sensing Image Based on Vector Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 309-317. DOI: 10.13203/j.whugis20190131
Citation: ZHANG Chunsen, LI Guojun, CUI Weihong. A Change Detection Method for Remote Sensing Image Based on Vector Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 309-317. DOI: 10.13203/j.whugis20190131

一种基于矢量数据的遥感影像变化检测方法

A Change Detection Method for Remote Sensing Image Based on Vector Data

  • 摘要: 为克服多时相遥感影像变化检测因成像季节、拍摄角度等因素产生的误差,针对影像变化通常少于未变化的情况,提出了一种利用旧时相矢量图与新时相影像进行变化检测的方法。利用旧时相带有类别信息的矢量约束,对新时相遥感影像进行细分割获得图斑,对图斑提取光谱、纹理等特征,通过主成分变换构建特征集,在类别的约束下,应用孤立森林计算图斑的变化指数,避免了传统变化指数计算方法因引入易受变化图斑干扰的值而产生的误差,同时,依据贝叶斯方法自动获取各地物类别的变化阈值,进而获得变化图斑。选取高分1号等数据分别进行两组实验,通过对比马氏距离与孤立森林方法,验证了采用矢量图与孤立森林方法进行变化检测的有效性,实验结果的正确率分别为0.923 5及0.931 8。

     

    Abstract:
      Objectives  Change detection is a process of recognizing the state changes of ground surface by multiple observations. With the improvement of image data quality, it provides more possibilities for people to realize change detection. Traditional multi-temporal remote sensing image change detection is easily affected by the season in which the images were taken, solar altitude angle and shooting angle etc. In addition, the traditional calculation methods of change index, variables which susceptible to the interference of change object are often introduced, such as mean value, median value and so on. To solve such problems, we proposed a change detection method based on vector data.
      Methods   Firstly, since traditional change detection method is always influenced by shooting season, shooting angle, etc, we use a vector-image method, which is different from the previous image-image method. With the application of this method, the change differences between the old and new images were calculated by using a similarity measurement method, and the change object will identified by a threshold value. The vector-image method using the vector data and the new time image data detect the changes. Secondly, the anomaly detection method in the data mining method can be introduced into the change detection method, hence the outlier has a fewer and different characteristics compared with the normal object. In this paper, we firstly got the object through incremental segmentation under the constraints of the previous vector image, then extracted its texture and spectral features to get the dataset by the principal component analysis transform. After that, using isolation forest method to calculate object's change index, we obtained the change threshold by Bayes method.
      Results  We took two experiments, and the effectiveness of the proposed method was verified by comparing image-image and vector-image change detection methods, as well as Markov distance and isolation forest change methods, among which, the accuracy rate of experiment 1 is 0.923 5 and that of experiment 2 is 0.931 8.
      Conclusions   By comparing the image-image method with the vector-image method, the experimental results show that the proposed method can not only improve the accuracy of change detection, but also improve the automation and intelligence of image-based change detection.

     

/

返回文章
返回