Journal of Geo-information Science ›› 2016, Vol. 18 ›› Issue (5): 599-605.doi: 10.3724/SP.J.1047.2016.00599

• Orginal Article • Previous Articles     Next Articles

Cloud and Cloud Shadow Multi-feature Collaborative Detection from Remote Sensing Image

SHEN Jinxiang1,2,*(), JI Xuan2   

  1. 1. Department of Digital Land and Land Management, Yunnan Land and Resources Vocational College, Kunming 652501, China
    2. Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China
  • Received:2016-03-08 Revised:2016-03-21 Online:2016-05-10 Published:2016-05-10
  • Contact: SHEN Jinxiang


Cloud and its shadow have certain degrees of impacts on the information extraction from remote sensing images. As the multi-source remote sensing data has become increasingly abundant in recent years, the cross application of the multi-source and multi-temporal remote sensing image for restoring the cloud and its shadow region, and for effectively acquiring the change information for the ground objects is an important content in studying the application of remote sensing big data. The precise detection of cloud and its shadow information is the premise and guarantee of their restoration. In general, the cloud and cloud shadow detection methods always use their spectral or spatial shape and the texture characteristics as references. However, regarding the complex and changeable spectrum and the inexpressible spatial shape characteristics, the cloud and cloud shadow information have always been difficult to be effectively detected. Based on the analysis of the spectral characteristics of thick clouds, thin clouds, snow and ice, and other feature types, a cloud and cloud shadow multi-feature collaborative detection method was proposed. (1) First of all, the cloud detection is executed. The proposed method extracts the standard thick cloud spectrum curve from the reflectance-calibrated image. Afterwards, the SAM method is used to match the cloud spectral curve from the distinguishable (red, shortwave infrared, thermal infrared) bands combination, with the absolute value of the shortwave infrared band pixel integrated to distinguish between cloud and snow, and the absolute value of the thermal infrared band pixel used to distinguish between cloud and other types of ground objects. (2) Next, the cloud shade detection is performed. Firstly, we expand the detected cloud pixel border, and produce a potential shadow mask area. Afterwards, we move the potential shadow mask along the direction of sun radiation to some distance. Thirdly, we detect the cloud shadow pixels using the brightness threshold of the near infrared band within the moved potential cloud shadow mask area. After several moves of the potential shadow mask and the implementations of cloud shadow detection based on the infrared band brightness threshold, eventually a complete cloud shadow mask is produced. The LANDSAT-8 image having the above mentioned bands is adopted in an experiment and the experimental result shows that the combination of spectral curve, "diagnosis" band and spatial relationship features can effectively detects the thin clouds, thick clouds and cloud shadows from the multispectral remote sensing image, and the overall accuracy is higher than 95%.

Key words: cloud detection, cloud shadow detection, multi-feature collaborative, threshold