一种基于U-Net的高分影像土地利用/覆盖变化检测方法

李聪毅; 孔祥兵; 杨娜; 王逸男; 杨刚凤

doi:10.13961/j.cnki.stbctb.2021.04.019

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一种基于U-Net的高分影像土地利用/覆盖变化检测方法

试验研究

- 一种基于U-Net的高分影像土地利用/覆盖变化检测方法
- A U-Net Based Land Use/Cover Change Detection Method with High Resolution Image
- 水土保持通报 2021年41卷第4期页码：133-138
- 作者机构：
  
  1. 河南理工大学测绘与国土信息工程学院,河南,焦作,454003
  2. 黄河水利科学研究院水利部黄土高原水土保持重点实验室,河南,郑州,450003
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目“基于同质区分析的高光谱影像混合像元稀疏分解研究”（61501200）;国家重点研发计划项目（2017YFC0504501）;河南省水利科技攻关计划项目（GG201942;GG201829）;黄科院研究开发项目（HKY-YFXM-2020-02）
- DOI：10.13961/j.cnki.stbctb.2021.04.019
  中图分类号： P237
- 纸质出版：2021
- 稿件说明：
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李聪毅, 孔祥兵, 杨娜, 等. 一种基于U-Net的高分影像土地利用/覆盖变化检测方法[J]. 水土保持通报, 2021,41(4):133-138.

Li Congyi, Kong Xiangbing, Yang Na, et al. A U-Net Based Land Use/Cover Change Detection Method with High Resolution Image[J]. Bulletin of Soiland Water Conservation, 2021, 41(4): 133-138.
李聪毅, 孔祥兵, 杨娜, 等. 一种基于U-Net的高分影像土地利用/覆盖变化检测方法[J]. 水土保持通报, 2021,41(4):133-138. DOI： 10.13961/j.cnki.stbctb.2021.04.019.

Li Congyi, Kong Xiangbing, Yang Na, et al. A U-Net Based Land Use/Cover Change Detection Method with High Resolution Image[J]. Bulletin of Soiland Water Conservation, 2021, 41(4): 133-138. DOI： 10.13961/j.cnki.stbctb.2021.04.019.

摘要

[目的

]

介绍一种基于U-Net的高分影像的土地利用/覆盖变化检测方法，为该模型在遥感影像变化检测方面的应用提供理论支持。[方法

]

采用U型神经网络对河南省禹州市两期高分一号影像和WHU building dataset建筑物变化检测数据集中的变化图斑进行自动检测试验，并与FCN和SegNet两种模型进行比较。[结果

]

在两个数据集的验证样本中，U型神经网络模型的F

值分别为0.699，0.66和0.673，均优于其他两种模型，并且漏检率较低，更加接近变化参考图。[结论

]

采用U型神经网络对高分辨率遥感影像进行土地利用/覆盖变化检测是可行的，且能有较高的检测精度。

Abstract

[Objective] The U-Net based land use/cover change detection method with high resolution image was introduced to provide theoretical support for the application of the model in remote sensing image change detection. [Methods] The U-type neural network was used to detect the change spots in Gaofen-1 image of Yuzhou City

He’nan Province and WHU building data

and compared with FCN and SegNet. [Results] The experimental results showed that the F1 score of U-type neural network model were 0.699

0.66 and 0.673 respectively

which were better than the other two methods

and the missing rate was lower

which was closer to the change reference diagram. [Conclusion] It is feasible to use U-type neural network for change detection in high-resolution remote sensing images

and it could have high detection accuracy.

关键词

Keywords

references

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