地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (4): 692-709.doi: 10.12082/dqxxkx.2021.200130

• 遥感科学与应用技术 • 上一篇    下一篇

基于特征增强和ELU的神经网络建筑物提取研究

唐璎1,2,3,4, 刘正军1,*, 杨懿1, 顾海燕1, 杨树文2,3,4   

  1. 1.中国测绘科学研究院摄影测量与遥感研究所,北京 100830
    2.兰州交通大学测绘与地理信息学院,兰州 730070
    3.地理国情监测技术应用国家地方联合工程研究中心,兰州 730070
    4.甘肃省地理国情监测工程实验室,兰州 730070
  • 收稿日期:2020-03-21 修回日期:2020-06-21 出版日期:2021-04-25 发布日期:2021-06-25
  • 通讯作者: *刘正军(1974— ),男,湖南湘潭人,研究员,主要从事遥感影像信息提取与生态环境遥感监测、突发事件应急地 理信息技术等研究。E-mail: zjliu@casm.ac.cn
  • 基金资助:
    国家重点研发计划项目(2018YFB0504504);国家自然科学基金项目(41701506、41371406);中央级公益性科研院所基本科研业务费专项资金项目(AR1923)

Research on Building Extraction based on Neural Network with Feature Enhancement and ELU Activation Function

TANG Ying1,2,3,4, LIU Zhengjun1,*, YANG Yi1, GU Haiyan1, YANG Shuwen2,3,4   

  1. 1. Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China
    2. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    3. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    4. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
  • Received:2020-03-21 Revised:2020-06-21 Online:2021-04-25 Published:2021-06-25
  • Contact: LIU Zhengjun
  • Supported by:
    The National Key R&D Program of China, No.2018YFB0504504(2018YFB0504504);N-ational Natural Science Foundation of China, No.41701506, 41371406(41701506、41371406);Central Public-interest Scientific Institution Basal Research Fund, No.AR1923(AR1923)

摘要:

近年来,城市发展快速,大量人口奔向城市工作生活,城市建筑物的数量有如雨后春笋般扩张,需要合理地规划城市土地资源,遏制违规乱建现象,因此基于高分辨率遥感影像,对建筑物进行准确提取,对城市规划和管理有着重要辅助作用。本文基于U-Net网络模型,使用美国马萨诸塞州建筑物数据集,对网络模型结构进行探究,提出了一种激活函数为ELU、“编码器-特征增强-解码器”结构的网络模型FE-Net。实验首先通过比较不同网络层数的U-Net5、U-Net6、U-Net7的建筑物提取效果,找到最佳的基础网络模型U-Net6;其次,基于该模型,加入特征增强结构得到“U-Net6+ReLU+特征增强”的网络模型;最后,考虑到ReLU容易产生神经元死亡,为优化激活函数,将激活函数替换为ELU,从而得到网络模型FE-Net(U-Net6+ELU+特征增强)。比较3个网络模型(U-Net6+ReLU、U-Net6+ReLU+特征增强、FE-Net(U-Net6+ELU+特征增强))的建筑物提取结果,表明FE-Net网络模型的建筑物提取效果最好,精度放松F1值达到97.23%,比“U-Net6+ReLU”和“U-Net6+ReLU+特征增强”2个网络模型分别高出0.36%和0.12%,且与其他具有相同数据集的研究成果比较,具有最高的提取精度,它能较好地提取出多尺度的建筑物,不仅对小尺度建筑物有较好的提取效果,而且能大致、较完整地提取出形状不规则的建筑物,有相对更少的漏检和错检,较准确地实现了端到端的建筑物提取。

关键词: 高分辨率遥感影像, 卷积神经网络, 建筑物提取, 特征增强, 激活函数ELU, FE-Net网络模型, 端到端, 深度学习

Abstract:

In recent years, with the rapid development of the city, a large number of people turn to work and live in the city, resulting in an increasing number of urban buildings. Land resources and urban ecological environment (such as green space) are threatened to some extent. Thus, it is urgent to plan urban land resources and space reasonably, prevent illegal construction, improve urban living environment, and make the city sustainable, orderly, healthy, and green. With the high-resolution remote sensing image data becoming more and more abundant, accurate building extraction using high-resolution remote sensing images plays an important role in urban planning, urban management, and change detection of urban buildings. Based on the U-Net network model, using the Massachusetts building dataset, this paper explored the network model structure and proposed a network model called FE-Net with "encoder-feature enhancement-decoder" structure and ELU activation function. First, the best basic network model called U-Net6 was found by comparing the building extraction results using U-Net5, U-Net6, and U-Net7 with different number of network layers. Based on the U-Net6, the network model of "U-Net6+ReLU+feature enhancement" was established by adding the structure of feature enhancement. In order to optimize the activation function, the ReLU activation function was replaced by the ELU activation function, and then the network model called FE-Net (U-Net6+ELU+feature enhancement) was created. The FE-Net network model was compared with the building extraction results from the other two network models (U-Net6+ReLU and U-Net6+ReLU+feature enhancement). Results show that the FE-Net network model had the best building extraction performance. Its relaxed F1-measure reached 97.23%, which was 0.36% and 0.12% higher than the other two network models. Meanwhile, FE-Net also had the highest extraction accuracy compared with other studies using the same dataset of Massachusetts. The FE-Net network model can extract multi-scale buildings better, which can not only extract small-scale buildings accurately, but also roughly and completely extract buildings with irregular shape with relatively less missing and wrong detections. Thus, the FE-Net network model can be used to achieve end-to-end building extraction with a high accuracy.

Key words: high-resolution remote sensing image, convolutional neural network, building extraction, feature enhancement, Exponential Linear Units (ELU), Feature Enhancement Network (FE-Net), end-to-end, deep learning