基于随机森林和形态学交替滤波的无人机影像建筑物提取  

Building Extraction of UAV Image Based on Aandom Forest and Morphological Alternating Filtering Building Index

作　　者：任文博[1] 绳鹏飞 帅明明 REN Wenbo;SHENG Pengfei;SHUAI Mingming(China Railway Shanghai Design Institute Group Co.,Ltd.,Shanghai 200070,China)

机构地区：[1]中铁上海设计院集团有限公司,上海200070

出　　处：《铁道勘察》2024年第4期26-34,共9页Railway Investigation and Surveying

基　　金：中国铁建股份有限公司科技开发计划重点课题(2023-Z03)。

摘　　要：如何从无人机获取的光学影像中快速、准确提取建筑物信息是遥感影像处理研究的热点问题。针对上述问题,在深入研究形态学建筑物指数、增强型形态学建筑物指数的基础上,结合特征提取、随机森林回归、改进的形态学交替滤波算法,对建筑物提取方法进行针对性改进。主要改进内容如下。(1)提出一种基于随机森林回归的无人机光学影像建筑物增强方法,通过融合原始可见光波段、可见光遥感指数特征、纹理特征等,构建特征空间,在不需要人工特征优选的情况下增强建筑物信息;(2)针对建筑物增强影像内部同质性区域会出现较多噪声等问题,对影像进行形态学滤波。最后,为验证该方法的有效性,选取环境复杂且具有代表性的城市中心建筑物区域与城市周边建筑物区域进行提取试验,2个区域主要建筑类型存在较大差别。在上述2个区域,增强型形态学建筑物指数算法的F1值较形态学建筑物指数算法分别提高10.40%和10.08%,较增强型形态学建筑物指数算法F1值分别提高5.23%和7.87%,表明该方法能够更好地提取出无人机光学影像中的建筑物信息。A way to quickly and accurately extract building information from visible light images acquired by drones is a hot issue in remote sensing image processing research.In response to the above problems,this paper combined feature extraction,random forest regression,and improved morphological alternating filtering algorithms on the basis of in-depth research on the morphological building index and enhanced morphological building index.A targeted improvement was made to the building extraction method.The main improvements in this paper are as follows.(1)A method for building enhancement of UAV visible light images based on random forest regression is proposed.By fusing the original visible light band,visible light remote sensing index features,texture features,etc.,the feature space is constructed,and no artificial feature selection is required while enhancing the building information in the case of the situation.(2)Aiming at the problem of a lot of noise in the homogeneous area inside the enhanced image of the building,the image is morphologically filtered through EASFs.Finally,in order to verify the effectiveness of the method in the paper,the representative city center building area and the city surrounding building area are selected for extraction experiments.The main building types of the two areas are quite different.In the above two areas,the F1 value of the proposed method is increased by 10.40%and 10.08%compared with the MBI algorithm,and the F1 of the first proposed an improved enhanced morphological building index value is increased by 5.23%and 7.87%compared with the EMBI algorithm.The results show that this method can better extract the building information from the UAV's visible light image.

关 键 词：无人机影像 特征图像 随机森林回归 EASFs滤波 形态学指数 

分 类 号：U212.2[交通运输工程—道路与铁道工程] P237[天文地球—摄影测量与遥感]