国土资源遥感2018，Vol.30Issue(1)：95-101,7.DOI:10.6046/gtzyyg.2018.01.13
利用KPCA法检测高分一号影像中的森林覆盖变化
The method for detecting forest cover change in GF-1images by using KPCA
摘要
Abstract
In order to study the methods for forest cover change monitoring by using GF-1 images, the Yajiang County in Sichuan Province was selected as the research area to extract the information of forest coverage based on the two GF-1 WFV data. Firstly,the data were normalized by using the iteration re-weight multivariate alteration detection(IR-MAD)method. The two images were transformed by kernel principal component analysis(KPCA) method,and formed differencing image. Then, the changed area was extracted using the method of maximum between class variances(OTSU)for automatic threshold selection. Finally, the change detection results were validated using OTSU with the field sample data, and the extracted results were verified by way of precision test, and comparatively analyzed with the change vector analysis(CVA). The research results show that the overall accuracy of the two change detection methods is higher than 80%,and the overall accuracy of the KPCA method is 89.27%. The user precision of unchanged area is 93.88%,and the user's accuracy of changed area is 80.28%. The accuracy of the KPCA method is better than that of the algorithm based on the traditional CVA method. It is shown that, after the data transformation, KPCA algorithm can reduce the correlation between the variables and enhance the signal to noise ratio of the image,thus improving the recognition accuracy for the changed area.关键词
高分一号(GF-1)/迭代加权多元变化检测(IR-MAD)/核主成分分析(KPCA)/最大类间方差法(OT-SU)Key words
GF-1/iteration re-weight multivariate alteration detection(IR-MAD)/kernel principal component analysis(KPCA)/method of maximum between class variance(OTSU)分类
信息技术与安全科学引用本文复制引用
尹凌宇,覃先林,孙桂芬,刘树超,祖笑锋,陈小中..利用KPCA法检测高分一号影像中的森林覆盖变化[J].国土资源遥感,2018,30(1):95-101,7.基金项目
国防科工局重大专项项目"高分林业遥感应用示范系统"(编号:21-Y30B05-9001-13/15)和民用航天预研项目"基于多源空间数据的森林火灾综合监测技术与应用示范"共同资助. （编号:21-Y30B05-9001-13/15）
