地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (10): 1959-1970.doi: 10.12082/dqxxkx.2020.190489

• 专栏:城乡生态环境综合监测 • 上一篇    下一篇

黑臭水体水面阴影提取的自适应阈值算法研究

许佳峰(), 李云梅*(), 徐杰, 雷少华, 毕顺, 周玲   

  1. 南京师范大学 虚拟地理环境教育部重点实验室,南京 210023
  • 收稿日期:2019-09-04 修回日期:2019-12-10 出版日期:2020-10-25 发布日期:2020-12-25
  • 通讯作者: 李云梅 E-mail:516787702@qq.com;liyunmei@njnu.edu.cn
  • 作者简介:许佳峰(1995— ),男,浙江嘉兴人,硕士生,主要从事水环境遥感研究。E-mail:516787702@qq.com
  • 基金资助:
    国家重点研发计划项目(2017YFB0503902)

Adaptive Threshold for Surface Shadow Detection of Black and Odor Water

XU Jiafeng(), LI Yunmei*(), XU Jie, LEI Shaohua, BI Shun, ZHOU Ling   

  1. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
  • Received:2019-09-04 Revised:2019-12-10 Online:2020-10-25 Published:2020-12-25
  • Contact: LI Yunmei E-mail:516787702@qq.com;liyunmei@njnu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503902)

摘要:

黑臭水体水面阴影对水面光谱信息产生干扰,严重地影响了利用高空间分辨率遥感数据进行水质状况监测的精度,因此,在数据预处理中必须进行阴影剔除。本研究基于无人机高光谱遥感数据,通过分析各种波段组合下黑臭水体水面的阴影像元和水体像元的光谱特征空间,选择以492、666和792 nm处的反射率建立黑臭水体的河面阴影指数(RSSI),并利用最大类间方差法(OTSU)自动确定划分本影、半影以及水体的阈值。利用南京金川河和龙江河的无人机高光谱遥感影像对算法进行测试,结果表明:RSSI阴影指数能突出显示阴影与水体的差异;OTSU自适应确定的阈值能较好地区分本影、半影和水体,阴影的总体识别精度达到85%以上。该算法能够有效地识别黑臭水体水面阴影,为后续开展水体的定性、定量遥感监测提供数据预处理的技术支持。

关键词: 水面阴影, 无人机高光谱影像, 阴影指数, OTSU, 本影, 半影, 黑臭水体

Abstract:

The shadow on black and odor water interfere with the spectral information of the water surface and seriously affects the accuracy of water quality monitoring with high spatial resolution remote sensing data. Therefore, it is necessary to remove the shadow before evaluating river water quality. This paper tries to constructan objective and efficient shadow recognition algorithm on black and odor water to reduce the interference of adjacent object and improve the accuracy of remote sensing monitoring and evaluation of river water quality. In this study, the shadow and water pixels were sampled based on the hyperspectral remote sensing data of Unmanned Aerial Vehicle (UAV).The spatial distribution of different band combinations was analyzed by means of spectral feature spatial analysis to obtain spectral band combinations that can effectively distinguish water and water surface shadows, and the coefficients of band combinations were calibrated to obtain the best discrimination effect. By comparing the discernibility of shadow and non-shadow water by various band combinations, it was found that the ration of remote sensing reflectance Rrs(666)/Rrs(791) combining with Rrs(492) has a higher discrimination between water pixels and shadow pixels. Therefore, remote sensing reflectance at 492 nm, 666 nm and 792 nm were selected to establish the River Surface Shadow Index (RSSI). In general, the threshold of distinguishing shadow and non-shadow pixels needs to be adjusted according to different images. In this case, manually adjusting the threshold may produce errors, which are difficult to apply to other images. In order to reduce the error caused by artificial threshold calibration, the maximum category variance method (OTSU)was adopted to automatically determine the threshold of shadow recognition. According to the complexity of the riverbank object, the reflectance spectra of the shadows were classified to two types: umbra and penumbra. The magnitude difference between penumbra and umbra reflectance was similar to that between penumbra and water reflectance. Therefore, in order to highlight the difference between penumbra and water, the number of classification recognition types was set as 3. Firstly, the OTSU method was used to automatically determine the recognition threshold of umbra, penumbra and water, and then the extracted umbra and penumbra were combined to produce the final shadow distribution map. The algorithm was tested by using the hyperspectral remote sensing images of Jinchuan River and Longjiang River in Nanjing. The results show that the RSSI shadow index can highlight the difference between shadow and water. The threshold determined by OTSU adaptively can better distinguish umbra, penumbra and water, and the overall recognition accuracy of shadow can reach more than 85%. This algorithm can effectively identify the water surface shadow on black and odor water and provide the technical support of data preprocessing for the subsequent qualitative and quantitative remote sensing monitoring for water.

Key words: shadow on water surface, hyperspectral image of UAV, shadow index, OTSU, umbra, penumbra, black and odor water