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It is highly desirable to accurately detect the clouds in satellite images before any kind of applications. However, clouds and snow discrimination in remote sensing images is a challenging task because of their similar spectral signature. The shortwave infrared (SWIR, e.g., Landsat TM 1.55–1.75 µm band) band is widely used for the separation of cloud and snow. However, for some sensors such as the CBERS-2 (China-Brazil Earth Resources Satellite), CBERS-4 and HJ-1A/B (HuanJing (HJ), which means environment in Chinese) that are designed without SWIR band, such methods are no longer practical. In this paper, a new practical method was proposed to discriminate clouds from snow through combining the spectral reflectance with the spatio-temporal contextual information. Taking the Mt. Gongga region, where there is frequent clouds and snow cover, in China as a case area, the detailed methodology was introduced on how to use the 181 scenes of HJ-1A/B CCD images in the year 2011 to discriminate clouds and snow in these images. Visual inspection revealed that clouds and snow pixels can be accurately separated by the proposed method. The pixel-level quantitative accuracy validation was conducted by comparing the detection results with the reference cloud masks generated by a random-tile validation scheme. The pixel-level validation results showed that the coefficient of determination (R2) between the reference cloud masks and the detection results was 0.95, and the average overall accuracy, precision and recall for clouds were 91.32%, 85.33% and 81.82%, respectively. The experimental results confirmed that the proposed method was effective at providing reasonable cloud mask for the SWIR-lacking HJ-1A/B CCD images. Since HJ-1A/B have been in orbit for over seven years and these satellites still run well, the proposed method is helpful for the cloud mask generation of the historical archive HJ-1A/B images and even similar sensors

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context