Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces

doi:10.3390/app10186467

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	Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces
	H. Y. Han, C. S. Han, T. J. Lan, L. Huang, C. H. Hu and X. C. Xue
	2020
发表期刊	Applied Sciences-Basel
卷号	10 期号:18 页码:25
摘要	Shadow often results in difficulties for subsequent image applications of multispectral satellite remote sensing images, like object recognition and change detection. With continuous improvement in both spatial and spectral resolutions of satellite remote sensing images, a more serious impact occurs on satellite remote sensing image interpretation due to the existence of shadow. Though various shadow detection methods have been developed, problems of both shadow omission and nonshadow misclassification still exist for detecting shadow well in high-resolution multispectral satellite remote sensing images. These shadow detection problems mainly include high small shadow omission and typical nonshadow misclassification (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification). For further resolving these problems, a new shadow index is developed based on the analysis of the property difference between shadow and the corresponding nonshadow with several multispectral band components (i.e., near-infrared, red, green and blue components) and hue and intensity components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), respectively. The shadow mask is further acquired by applying an optimal threshold determined automatically on the shadow index image. The final shadow image is further optimized with a definite morphological operation of opening and closing. The proposed algorithm is verified with many images from WorldView-3 and WorldView-2 acquired at different times and sites. The proposed algorithm performance is particularly evaluated by qualitative visual sense comparison and quantitative assessment of shadow detection results in comparative experiments with two WorldView-3 test images of Tripoli, Libya. Both the better visual sense and the higher overall accuracy (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) of the experimental results together deliver the excellent performance and robustness of the proposed shadow detection approach for shadow detection of high-resolution multispectral satellite remote sensing images. The proposed shadow detection approach is promised to further alleviate typical shadow detection problems of high small shadow omission and typical nonshadow misclassification for high-resolution multispectral satellite remote sensing images.
DOI	10.3390/app10186467
URL	查看原文
收录类别	SCI
语种	英语
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64326
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. Y. Han, C. S. Han, T. J. Lan, L. Huang, C. H. Hu and X. C. Xue. Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces[J]. Applied Sciences-Basel,2020,10(18):25.
APA	H. Y. Han, C. S. Han, T. J. Lan, L. Huang, C. H. Hu and X. C. Xue.(2020).Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces.Applied Sciences-Basel,10(18),25.
MLA	H. Y. Han, C. S. Han, T. J. Lan, L. Huang, C. H. Hu and X. C. Xue."Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces".Applied Sciences-Basel 10.18(2020):25.

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