Interpretation method for regional co-seismic collapses based on multi-feature fusion of optical remote sensing
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摘要:
同震崩塌滑坡的解译及定位是震区灾后恢复工作中需要重点解决的问题，如何在灾害快速、自动解译的基础上，不断提高解译精度，是目前同震崩塌滑坡解译的研究热点之一，也是促使地质灾害早期识别向智能化、科学化发展的必要前提。文章在团队前期所提出的遥感影像局部阈值二值化方法的基础上，针对同震崩塌滑坡解译结果假阳率偏高的问题，分析了假阳性地物的光学和几何特点，提出了融合目标区域光学影像灰度特征、区域坡度信息、NDVI指数特征及解译地物主轴特征的同震崩塌滑坡多特征融合解译方法。为验证所提出模型的准确性，以2014年云南鲁甸地震龙头山镇为研究区，利用震后获取的高分一号（GF-1）卫星影像数据及数字高程模型对该同震崩塌滑坡进行了解译识别，结果表明，文中提出的方法准确解译出了同震崩塌滑坡区域，并有效去除了假阳性地物干扰，提高了解译精度。
Abstract:Interpretation of co-seismic collapse landslides is a key problem that needs to be solved in the post-disaster recovery work in earthquake areas. The issue regarding continuously improvement of interpretation accuracy for rapid and automatic interpretation of disasters is currently a hot topic, which is also a prerequisite to promote the development of early recognition of geological disasters towards intelligence and scientific. Based on the local threshold binarization method of remote sensing image proposed by the team in the early stage, this paper analyzes the optical and geometric characteristics of false positive features and proposes a fusion for the high false positive rate of the interpreted results of co-seismic mountain collapse. The multi-feature fusion interpretation method of the co-seismic mountain collapse with the gray feature of the optical image of the target area, the regional slope information, the NDVI feature and the interpretation of the main axis feature of the ground feature. In order to verify the accuracy of the proposed model, based on the 2014 Ludian earthquake in Yunnan, a case study was carried out in the Longtoushan town area. The Gaofen-1 (GF-1) satellite image data obtained after the earthquake and the digital elevation model were used for the earthquake in this area. The interpretation and recognition of the collapse of the cracked mountain shows that the method proposed in this paper accurately interprets the collapsed area of the cracked mountain body, effectively removes the false positive ground object interference, and improves the accuracy of interpretation.
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表 1 原始数据参数信息表
Table 1. Information of the input data
原始数据 谱段范围/μm 空间分辨率/m 高分一号（GF-1）
卫星影像数据0.45～0.90 2.00 0.45～0.52 8.00 0.52～0.59 0.63～0.69 0.77～0.89 数字高程模型DEM − 30.00 
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