水环境评价与修复治理

无人机高光谱影像水面耀光去除及信息重构方法研究

  • 王世瑞 ,
  • 沈芳 ,
  • 李仁虎 ,
  • 李鹏
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  • 华东师范大学 河口海岸学国家重点实验室, 上海 200241

收稿日期: 2022-11-25

  录用日期: 2023-04-06

  网络出版日期: 2024-01-23

基金资助

国家自然科学基金(42076187)

Research on water surface glint removal and information reconstruction methods for unmanned aerial vehicle hyperspectral images

  • Shirui WANG ,
  • Fang SHEN ,
  • Renhu LI ,
  • Peng LI
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  • State Key Laboratory of Estuarine and Coastal Research, East China Normal University,Shanghai 200241, China

Received date: 2022-11-25

  Accepted date: 2023-04-06

  Online published: 2024-01-23

摘要

抑制遥感影像水面耀光污染并重构影像信息, 是改善无人机遥感信息质量、扩大水环境监测区域的有效途径. 针对传统经典的耀光信息重构算法难以适用于无人机高光谱影像这一问题, 提出了一种耀光自动检测去除与信息重构算法, 即采用归一化水体指数提取水体, 以全波段总和灰度图像的最低值为阈值对耀光进行分割, 利用拉普拉斯算子提取水面耀光纹理, 通过多轮形态学膨胀与阈值更新迭代计算出两者面积差值, 以投票机制获得最小差值的出现频率, 并逆向获取最佳阈值自动去除耀光. 而后, 基于主成分分析确定匹配波段, 通过改进Criminisi算法对去除区域进行重构. 去除算法应用于四个真实耀光场景, 去除率均在99%以上. 重构算法结果在主观和客观上均优于其他算法, 耀光重构水体与正常水体各波段变异系数差值在1%以内, 具有良好的光谱应用能力.

本文引用格式

王世瑞 , 沈芳 , 李仁虎 , 李鹏 . 无人机高光谱影像水面耀光去除及信息重构方法研究[J]. 华东师范大学学报(自然科学版), 2024 , 2024(1) : 36 -49 . DOI: 10.3969/j.issn.1000-5641.2024.01.005

Abstract

Suppressing water glint pollution from remote sensing images and reconstructing image information are effective ways to improve the quality of UAV (unmanned aerial vehicle) remote sensing information and increase water environment monitoring areas. It is difficult to apply traditional glint information reconstruction algorithms to UAV hyperspectral images. This study proposes an algorithm for automatic glint detection, removal, and information reconstruction. First, NDWI (normalized difference water index) was used to extract the water body, and the lowest value of the sum of grayscale images in the entire band was used as a threshold to segment the glint, and the Laplace operator was used to extract the glint texture. The difference between the two areas was calculated through multiple rounds of morphological expansion and threshold updates. The lowest difference occurrence frequency was obtained by voting, and the best threshold was obtained in reverse to remove the glint automatically. Then, we determined the matching bands based on principal component analysis and compared the minimum similarity of matching blocks of different sizes to obtain the best size of the image blocks. Finally, we used an improved Criminisi algorithm to reconstruct the flare removal region. The removal algorithm was applied to four real glint scenarios with a removal rate > 99%; the reconstruction algorithm results are superior to those of other algorithms both subjectively and objectively, and the difference between the variation coefficient of each band of the glint reconstruction for water and normal water was within 1%, indicating good spectral application capability.

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