 |
| Titel |
An automated cloud detection method based on the green channel of total-sky visible images |
| VerfasserIn |
J. Yang, Q. Min, W. Lu, W. Yao, Y. Ma, J. Du, T. Lu, G. Liu |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 11 ; Nr. 8, no. 11 (2015-11-05), S.4671-4679 |
| Datensatznummer |
250116677
|
| Publikation (Nr.) |
 copernicus.org/amt-8-4671-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Obtaining an accurate cloud-cover state is a challenging task. In the past,
traditional two-dimensional red-to-blue band methods have been widely used
for cloud detection in total-sky images. By analyzing the imaging principle
of cameras, the green channel has been selected to replace the 2-D red-to-blue
band for detecting cloud pixels from partly cloudy total-sky images in this
study. The brightness distribution in a total-sky image is usually
nonuniform, because of forward scattering and Mie scattering of aerosols,
which results in increased detection errors in the circumsolar and
near-horizon regions. This paper proposes an automatic cloud detection
algorithm, "green channel background subtraction adaptive threshold"
(GBSAT), which incorporates channel selection, background simulation,
computation of solar mask and cloud mask, subtraction, an adaptive threshold,
and binarization. Five experimental cases show that the GBSAT algorithm
produces more accurate retrieval results for all these test total-sky
images. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|