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摘要

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引用本文:

DOI:

10.11834/jrs.20211209

收稿日期:

2021-04-13

修改日期:

2021-07-19

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参数缺失下的多光谱卫星影像云与云影检测
胡昌苗, 张正, 唐娉
中国科学院 空天信息创新研究院
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,本文研究在参数缺失情况下的GF-1 WFV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。

Multispectral Satellite Image Cloud and Cloud Shadow Detection with Missing Parameters
Abstract:

Objective: The existence of 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 multi-spectral satellite images, but 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, but a lot of Chinese satellites’ images have lost parameter auxiliary files during multiple product iterations, and many military application satellite images are missing or do not provide parameter files. Multispectral satellite image cloud and cloud shadow detection with missing parameters require special researches. Method: This paper studies the cloud and cloud shadow detection method of domestic four-band multi-spectral satellite imagery with missing related parameters. This algorithm process is based on the classic spectral threshold cloud and cloud shadow detection algorithm, and uses image processing and morphological algorithms to improve detection accuracy. For the data with missing parameters, a morphology-based method for estimating the azimuth and distance of the cloud shadow relative to the cloud area is proposed. Result: The experimental data in this paper is from the Gaofen-1 (GF-1) satellite WFV sensor, and the 86 test images are from Dunhuang, Gansu, China. The experimental area contains a large area of bright surface and snow-capped mountains that are easy to be misdetected in cloud and cloud shadow detection. The result of the cloud and cloud shadow detection experiment of this paper in the case of missing parameters achieves similar accuracy to normal algorithms. At the same time, this paper analyzes the misdetection of the algorithm and clarifies the challenges of subsequent researches. Conclusion: In this paper we propose a set of refined cloud and cloud shadow detection algorithms in case of missing parameters for domestic four-band multi-spectral satellite imagery. The algorithms are based on the classical spectral threshold cloud and cloud shadow detection algorithm and use image processing and morphological algorithms to further improve accuracy. 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 in the case of missing parameters, the detection results of this algorithm achieve an accuracy similar to that of the widely used MFC algorithm.

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