计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 140-145.doi: 10.11896/jsjkx.200800002

• 计算机图形学&多媒体 • 上一篇    下一篇

基于边缘特征融合的高分影像建筑物目标检测

赫晓慧1, 邱芳冰2, 程淅杰2, 田智慧1, 周广胜3   

  1. 1 郑州大学地球科学与技术学院 郑州450052
    2 郑州大学信息工程学院 郑州450001
    3 中国气象科学研究院郑州大学生态气象联合实验室 郑州450052
  • 收稿日期:2020-08-01 修回日期:2020-09-10 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 赫晓慧(hexh@zzu.edu.cn)
  • 基金资助:
    第二次青藏高原综合科学考察研究项目(2019QZKK0106)

High-resolution Image Building Target Detection Based on Edge Feature Fusion

HE Xiao-hui1, QIU Fang-bing2, CHENG Xi-jie2, TIAN Zhi-hui1, ZHOU Guang-sheng3   

  1. 1 School of Earth Science and Technology,Zhengzhou University,Zhengzhou 450052,China
    2 School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
    3 Joint Laboratory of Eco-Meteorology,Chinese Academy of Meteorological Sciences,Zhengzhou University,Zhengzhou 450052,China
  • Received:2020-08-01 Revised:2020-09-10 Online:2021-09-15 Published:2021-09-10
  • About author:HE Xiao-hui,born in 1978,professor.Her main research interests include artificial intelligence,computer vision,remote sensing image processing,and data mining.
  • Supported by:
    Second Tibetan Plateau Scientific Expedition and Research(STEP) Program(2019QZKK0106)

摘要: 高分辨率遥感图像建筑物目标检测在国土规划、地理监测、智慧城市等领域有着广泛的应用价值,但是由于遥感图像背景复杂,建筑物目标的部分细节特征与背景区分度较低,在进行检测任务时,容易出现建筑物轮廓失真、缺失等问题。针对这一问题,设计了自适应加权边缘特征融合网络(VAF-Net)。该方法针对遥感图像建筑物检测任务,对经典编解码器网络U-Net进行拓展,通过融合RGB特征图和边缘特征图,弥补了基础网络学习中的细节特征缺失;同时,借助网络的学习自动更新融合权重,实现自适应加权融合,充分利用不同特征的互补信息。该方法在Massachusetts Buildings数据集上进行了实验,其准确率、召回率和F1-score分别达到了82.1%,82.5%和82.3%,综合指标F1-score相比于基础网络提升了约6%。VAF-Net有效提高了编解码器网络对于高分影像建筑物目标检测任务的表现性能,具有良好的实用价值。

关键词: U-Net, 边缘特征, 目标检测, 神经网络, 特征融合

Abstract: High-resolution remote sensing image building target detection has a wide range of application value in territorial planning,geographic monitoring,smart city and other fields.However,due to the complex background of remote sensing images,some detailed features of building targets are less distinguishable from the background.During the task,it is prone to problems such as distortion and missing of the building outline.Aiming at this problem,an adaptive weighted edge feature fusion network (VAF-Net) is designed.This method is aimed at remote sensing image building detection tasks,expands the classic codec network U-Net network,and makes up for the lack of detailed features in basic network learning through the fusion of RGB feature maps and edge feature maps.At the same time,relying on network learning to automatically update the fusion weight,adaptive weighted fusion can be achieved,and the complementary information of different features can be full made use of.This method is tested on the Massachusetts Buildingsdata set,and its accuracy,recall and F1-score reach 82.1%,82.5% and 82.3%,respectively.The comprehensive index F1-score increases by about 6% compared to the basic network.VAF-Net effectively improves the perfor-mance of the codec network for high-resolution image building target detection tasks,and has good practical value.

Key words: Edge feature, Feature fusion, Neural network, Target detection, U-Net

中图分类号: 

  • TP391.4
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