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Remote Sens. 2016, 8(1), 31; doi:10.3390/rs8010031

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

1,2,†
,
1,†,* , 1,2,†
and
3
1
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Geography, University of Maryland, College Park, MD 20742, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 27 September 2015 / Revised: 14 December 2015 / Accepted: 25 December 2015 / Published: 5 January 2016
View Full-Text   |   Download PDF [13244 KB, uploaded 5 January 2016]   |  

Abstract

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. View Full-Text
Keywords: cloud; snow; HJ-1A/B; regional covariance matrix; spectral; spatio-temporal; texture; context cloud; snow; HJ-1A/B; regional covariance matrix; spectral; spatio-temporal; texture; context
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bian, J.; Li, A.; Liu, Q.; Huang, C. Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context. Remote Sens. 2016, 8, 31.

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