Water Change Monitoring by Object-oriented Detection Based on Unmanned Aerial Vehicle Image

QIN Huijie; GAO Lei; LIANG Wenguang; OUYANG Xiao

doi:10.13961/j.cnki.stbctb.2018.05.041

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Water Change Monitoring by Object-oriented Detection Based on Unmanned Aerial Vehicle Image

- Water Change Monitoring by Object-oriented Detection Based on Unmanned Aerial Vehicle Image
- Bulletin of Soiland Water Conservation Vol. 38, Issue 5, Pages: 256-260(2018)
- 作者机构：
  
  1. 江苏省测绘工程院,江苏,南京,210013
  2. 卫星测绘技术与应用国家测绘地理信息局重点实验室,江苏,南京,210013
  3. 江苏省水利科学研究院湖泊所,江苏,南京,210017
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2018.05.041
  CLC： P208
- Published：2018
- 稿件说明：
移动端阅览
QIN Huijie, GAO Lei, LIANG Wenguang, et al. Water Change Monitoring by Object-oriented Detection Based on Unmanned Aerial Vehicle Image[J]. Bulletin of Soiland Water Conservation, 2018, 38(5): 256-260.
DOI：

QIN Huijie, GAO Lei, LIANG Wenguang, et al. Water Change Monitoring by Object-oriented Detection Based on Unmanned Aerial Vehicle Image[J]. Bulletin of Soiland Water Conservation, 2018, 38(5): 256-260. DOI： 10.13961/j.cnki.stbctb.2018.05.041.

摘要

[目的]针对大多无人机影像仅有红、绿、蓝3个波段的特点，提出一种利用纹理特征提取水体并进行变化监测的方法，为水利部门了解水体面积变化情况提供技术支持。[方法]首先利用易康软件对无人机影像进行多尺度分割，形成影像像斑，再计算影像像斑的角二阶矩、均值、熵等纹理特征，将这些纹理特征作为影像波段进行组合，从而突出水体，最后通过ISO分类的方法提取出水体，将不同时期提取的水体进行叠置分析，得到水体的变化区域。[结果]通过选择不同的区域进行试验，水体的提取精度和变化检测精度均达90%以上。[结论]面向对象的无人机影像水体变化监测方法可有效提高水体变化监测的效率，节约人工作业量。

Abstract

[Objective] Due to the fact that there are only three bands (red

green and blue) in most UAV(unmanned aerial vehicle)images

a method of extracting water body and monitoring water changes based on texture feature is proposed in order to provide technical support for water conservancy department.[Methods] We used eCognition software to divide the UAV images into multiple scales

and from the image object firstly. The angular second-order moment

mean

entropy and other texture features of the image object was then calculated. These texture features was combined as the image band to highlight the water body. Finally

the water body was extracted by ISO classification method

and the water body extracted from different periods was analyzed to obtain the change area of the water body.[Results] By testing in different regions

the accuracy of the water extraction and detection for water change was above 90%.[Conclusion] The proposed method can effectively improve the efficiency of the water body change monitoring

and reduce the amount of manual labor.

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references

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杜敬.基于深度学习的无人机遥感影像水体识别[J].江西科学,2017,35(1):158-161,170.

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张剑清,朱丽娜,潘励.基于遥感影像和矢量数据的水系变化检测[J].武汉大学学报:信息科学版,2007,32(8):663-666.

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