Integration of SAR polarimetric parameters and multi-spectral data for object-based land cover classification

被引：0

作者：

Zhao Y. ^{[1
]}

Jiang M. ^{[1
]}

机构：

[1] School of Earth Sciences and Engineering, Hohai University, Nanjing

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2019年 / 48卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Data fusion; Land-cover classification; Multispectral; Object-based; Polarimetric; Synthetic aperture radar(SAR);

D O I：

10.11947/j.AGCS.2019.20170746

中图分类号：

学科分类号：

摘要：

An object-based approach is proposed for land cover classification using optimal polarimetric parameters. The ability to identify targets is effectively enhanced by the integration of SAR and optical images. The innovation of presented method can be summarized in the following two main points: ① estimating polarimetric parameters (H-A-α decomposition) through optical image as a driver; ② a multi-resolution segmentation based on optical image only is deployed to refine classification results. The proposed method is verified by using Sentinel-1/2 datasets over Bakersfield area, California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database (NLCD). A detailed accuracy assessment complied for seven classes of surfaces shows that the proposed method outperforms the conventional approach by around 10%, with an overall accuracy of 92.6% over regions with rich texture. © 2019, Surveying and Mapping Press. All right reserved.

引用

页码：609 / 617

页数：8

共 45 条

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