热带亚热带植被覆盖区的光学遥感云检测提取方法

doi:10.6046/zrzyyg.2024151

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摘要

热带亚热带植被覆盖地区的光学卫星遥感影像往往受到云影响,导致地物遥感信息缺失,如何有效地进行云检测、云物分类和云覆盖信息提取,仍然是遥感领域研究的热点和难点。国产卫星许多光学相机缺少目前主流云检测算法中的短波红外和热红外谱段,极大程度地降低了数据云去除的可用性。基于此,该文提出了一种仅利用可见光-近红外(400~1 000 nm)范围的几个谱段来实现云覆盖空间分布监测的算法。基于珠海一号卫星高光谱遥感影像,结合归一化水体指数(normalized difference water index,NDWI)和归一化植被指数(normalized difference vegetation index,NDVI)的光谱指数构建特征空间散点图,进行云/物分类和云检测,并提取混合像元中云、水和植被各组分覆盖信息。结果表明,相较于常规云检测的光谱指数阈值法,该文提出的基于NDWI-NDVI特征空间的云检测算法有更好的云/水分离能力,可以抑制阴影对云覆盖影响,精确描绘云覆盖空间分布特征,且简便易行,这为进一步发展国产光学卫星遥感数据云检测、云/水分离和云覆盖信息提取算法拓展了新的研究思路和技术途径。

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	黄飞
	王萧琼
	聂冠瑞
	颜军
	李先怡
	田家
	朱翠翠
	李前景
	田庆久

关键词 ：光谱指数, 高光谱遥感, 云检测, 特征空间, 热带亚热带, 珠海一号卫星

Abstract：

Optical satellite remote sensing images of tropical and subtropical vegetation areas are often affected by cloud cover, leading to missing remote sensing information of surface features. Effectively detecting clouds, classifying clouds and objects, and extracting cloud cover information remain hot topics and challenges in remote sensing research. Many optical cameras in domestic satellites lack the short-wave and thermal infrared spectral bands, which are used in prevailing cloud detection algorithms, reducing the image data availability after cloud removal. Hence, this study suggested detecting the spatial distribution of cloud cover by utilizing only several spectral bands in the visible light - near-infrared range (400 nm to 1 000 nm). Based on the hyperspectral remote sensing images from the Zhuhai-1 satellite, this study constructed feature space scatter plots using spectral indices, including normalized difference vegetation index (NDVI) and normalized differential water index (NDWI), for cloud/object classification and detection. Moreover, this study extracted the cloud, water, and vegetation cover information from mixed pixels. The results demonstrate that compared to conventional cloud detection methods using spectral index thresholds, the cloud detection algorithm under the NDWI-NDVI feature space used in this study exhibited a superior cloud-water separation capability and simple operability. It can precisely describe the spatial distribution characteristics of cloud cover by suppressing the shadow effect on cloud cover. Overall, this study offers a novel technical approach for further developing cloud detection, cloud-water separation, and cloud cover information extraction algorithms for domestic optical satellite remote sensing data.

Key words： spectral index hyperspectral remote sensing cloud detection feature space tropic and subtropic Zhuhai-1 satellite

收稿日期: 2024-04-19 出版日期: 2025-09-03

ZTFLH:

TP79

基金资助:国家重点研发计划重点专项课题“星载波形LiDAR和多角度光学协同森林AGB估测技术”(2023YFF1303903);自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2022-07-018)

作者简介: 黄飞(2000-),男,硕士研究生,主要从事高光谱和热红外遥感研究。Email: 502023270130@smail.nju.edu.cn。

引用本文:

黄飞, 王萧琼, 聂冠瑞, 颜军, 李先怡, 田家, 朱翠翠, 李前景, 田庆久. 热带亚热带植被覆盖区的光学遥感云检测提取方法[J]. 自然资源遥感, 2025, 37(4): 58-67.
HUANG Fe, WANG Xiaoqiong, NIE Guanrui, YAN Jun, LI Xianyi, TIAN Jia, ZHU Cuicui, LI Qianjing, TIAN Qingjiu. Optical remote sensing-based cloud detection and extraction method for tropical and subtropical vegetation areas. Remote Sensing for Natural Resources, 2025, 37(4): 58-67.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024151 或 https://www.gtzyyg.com/CN/Y2025/V37/I4/58

Fig.1 研究区位置示意图

Fig.2 研究区珠海一号卫星高光谱假彩色和真彩色影像

Fig.3 云/物反射光谱曲线

Fig.4 基于NDVI和NDWI直方图阈值及其分割图像

Tab.1 NDVI和NDWI光谱指数分割阈值特征点

Fig.5 NDWI-NDVI特征空间散点图

Fig.6 NDVI-NDWI特征空间阈值分类过程

Fig.7 基于单一光谱指数阈值的云物分类图

Fig.8 NDWI-NDVI散点分类结果

Fig.9 基于光谱指数三角特征空间的云物覆盖度分布图

Fig.10 云检测和云提取方法效果对比图

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