国产卫星多光谱数据云与云影检测算法研究
Research on multispectral satellite image cloud and cloud shadow detection algorithm of domestic satellite
- 2023年27卷第3期 页码：623-634
纸质出版日期： 2023-03-07
DOI： 10.11834/jrs.20211209
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摘要
云与云影降低了遥感数据的应用价值，对多光谱卫星影像进行精确、自动的云与云影检测与标记有利于遥感影像的后续应用。中国目前有海量的高分辨率多光谱卫星影像，但卫星数据产品中很少包含逐像素的云与云影标记数据。高质量的云检测算法通常需要卫星成像几何、时间与定标系数等参数，但很多国产数据在多次产品迭代过程中丢失了参数辅助文件，很多军事应用卫星数据缺失或不提供参数文件，本文研究在参数缺失情况下的国产四波段多光谱卫星影像云与云影检测方法，以经典光谱阈值云与阴影检测算法为基础，利用图像处理与形态学算法改善精度，并对于缺失参数的数据提出了一种基于形态学的云影相对云区方位与距离的估算方法。实验数据利用包含高亮地表与雪山的中国甘肃敦煌区域86景高分一号卫星图像，结果表明在缺失参数的情况下本文算法检测结果依然能达到与常用算法相近的精度，同时本文也对算法存在的误检情况进行了分析，明确后续研究的挑战。
Abstract
The existence of a cloud reduces the application value of remote sensing images. Accurate and automatic cloud and cloud shadow detection and labeling for multispectral satellite images is conducive to the subsequent application of remote sensing images. China currently has a large number of high-resolution multispectral satellite images. However
standard data products rarely contain pixel-by-pixel cloud and cloud shadow tag data for quality analysis. Traditional cloud detection algorithms usually require parameters
such as satellite imaging geometry
imaging time
and calibration coefficients. However
many Chinese satellites’ images have lost parameter auxiliary files during multiple product iterations. Moreover
many military application satellite images are missing or do not provide parameter files. Multispectral satellite image cloud and cloud shadow detection with missing parameters requires special research.
The present study investigates the cloud and cloud shadow detection method of domestic four-band multispectral satellite imagery with missing related parameters. This algorithm process is based on the classic spectral threshold cloud and cloud shadow detection algorithm. It also uses image processing and morphological algorithms to improve detection accuracy. A morphology-based method for estimating the azimuth and distance of the cloud shadow relative to the cloud area is proposed for the data with missing parameters.
The experimental data in this study is from the GF-1 satellite Wide-Field View (WFV) sensor
and the 86 test images are from Dunhuang
Gansu
China. The experimental area contains a large area of a bright surface and snow-capped mountains that are easily misdetected in cloud and cloud shadow detection. The result of the cloud and cloud shadow detection experiment in this study for the case of missing parameters achieves accuracy similar to that achieved by normal algorithms. This study also analyzes the misdetection of the algorithm and clarifies the challenges of subsequent research.
In this study
we propose a set of refined cloud and cloud shadow detection algorithms in case of missing parameters for domestic four-band multispectral satellite imagery. The algorithms are based on the classical spectral threshold cloud and cloud shadow detection algorithms. They also use image processing and morphological algorithms to improve accuracy further. Moreover
a morphology-based method for estimating the orientation and distance of cloud shadow relative to the cloud area is proposed for the data with missing parameters. The experimental results of GF-1 WFV data show that the detection results of this algorithm achieve an accuracy similar to that of the widely used MFC algorithm in the case of missing parameters.
关键词
云检测阴影检测高分一号多光谱影像参数缺失
Keywords
cloud detectioncloud shadow detectionGF-1multispectral satellite imagemissing parameters
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中国科学院大学 电子电气与通信工程学院
自然资源部 空间海洋遥感与应用重点实验室
自然资源部 国家卫星海洋应用中心
青岛海洋科学与技术试点国家实验室 区域海洋动力学与数值模拟功能实验室
中国海洋大学 三亚海洋研究院
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