结合深度学习技术的遥感影像城市建筑物自动提取  

Automatic Extraction of Urban Buildings from Remote Sensing Images Combined with Deep Learning Technology

作　　者：杨典华 黄瀚轩 

机构地区：[1]北京高创数科科技有限公司技术部,北京 [2]北京八一学校国际部,北京

出　　处：《测绘科学技术》2024年第4期326-335,共10页Geomatics Science and Technology

摘　　要：背景:目前建筑物提取的自动化程度仍然不够高,原有的机器学习算法无法满足建筑物提取的需求。深度学习技术的兴起为信息提取提供了新的方法。本文目的是研究遥感影像城市建筑物的自动提取,以达到近似人工矢量提取的效果,方法是以深度学习技术为基础,研究1 m空间分辨率数据,通过分辨率增强,得到0.5 m、0.25 m空间分辨率数据,采用DeepLab v2深度卷积神经网络,进行实验分析,并通过矢量简化的方法,研究提取结果的后期优化,以符合对建筑物提取的要求。结果发现通过分辨率增强方法,像素精度提高了7.9%,交并比提高了11.8%,在此基础上,结合矢量优化方法,对提取的建筑物边缘进行优化,结果更加符合建筑物提取需求。结论是针对深度学习技术特点,进行分辨率增强可以提高信息提取效果,结合矢量方法进行优化,可以改善建筑物提取效果。研究应用意义及改进方向:将深度学习技术应用于遥感信息提取,可使遥感信息提取的精度更高。未来将对遥感影像特征进行更深入的优化研究。Background: At present, the level of automation of building extraction is still not high enough. The original machine learning algorithms cannot meet the needs of building extraction. The rising of deep learning technology provides new methods for information extraction. Objective: Study the automatic extraction of remote sensing image urban buildings to achieve the effect of approximate artificial vector extraction. Methods: The DeepLab v2 deep convolutional neural network is used for experimental analysis, and the vector optimization method is used to study the later optimization of the extraction results to meet the requirements for building extraction. Experimental content: The original data is subjected to resolution enhancement of the original data to obtain spatial resolution data of 0.5 m and 0.25 m. Results: Over-resolution enhancement method improves pixel accuracy

关 键 词：深度学习 建筑物提取 分辨率增强 矢量优化 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]