Abstract
In polar regions, cloud and underlying ice-snow areas are difficult to distinguish in satellite images because of their high albedo in the visible band and low surface temperature of ice-snow areas in the infrared band. A cloud detection method over ice-snow covered areas in Antarctica is presented. On account of different texture features of cloud and ice-snow areas, five texture features are extracted based on GLCM. Nonlinear SVM is then used to obtain the optimal classification hyperplane from training data. The experiment results indicate that this algorithm performs well in cloud detection in Antarctica, especially for thin cirrus detection. Furthermore, when images are resampled to a quarter or 1/16 of the full size, cloud percentages are still at the same level, while the processing time decreases exponentially.
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References
Curry J A, Rossow W B, Randall D, et al.(1996)Overview of Arctic cloud and radiation characteristics[J]. Journal of Climate, 9(8):1 731–1 764
Griffin M, Burke H H, Mandl D, et al.(2003)Cover detection algorithm for EO-1 hyperion imagery[J]. Geoscience and Remote Sensing Symposium, 7(1): 86–89
Gao Bocai, Yang Ping, Li Rongrong(2003)Detection of high clouds in polar regions during the daytime using the MODIS 1.375 μm channel [J]. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 474–481
Choi H, Bindschadler R(2004)Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision [J]. Remote Sensing of Environment, 91(2):237–242
Muramoto K I, Saito H, Matsuura K, et al.(1997) Cloud and ice detection using NOAA/AVHRR data[C]. IGARSS’97, Toronto, Canada
McIntire T J, Simpson J J(2002)Arctic sea ice, cloud, water, and lead classification using neural networks and 1.6 μm data[J]. IEEE Transactions on Geoscience and Remote Sensing, 40(9):1 956–1 972
Haralick R M, Shanmugam K, Dinstein I(1973)Textural features for image classification[J]. IEEE Transactions on Systems Man and Cybernetics, 3(6):610–621
Vapnik V N(1995)The nature of statistical learning theory [M]. New York: Springer
Cristianini N, Shawe-Taylor J(2000)An introduction to support vector machines [M]. Cambridge: Cambridge University Press
Luo Jianchen, Zhou Chenhu, Liang Yi, et al. (2002) Support vector machine for spatial feature extraction and classification of remotely sensed imagery[J]. Journal of Remote Sensing, 6(1): 50–55 (in Chinese)
Gan Xinzhen, Sun Jiabing(1994)The removal of the streaking noise on Antarctica satellite image[J]. Journal of Wuhan Technical University of Surveying and Mapping, 19(4): 332–334 (in Chinese)
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Supported by the Antarctic Geography Information Acquisition and Environmental Change Research of China (No.14601402024-04-06).
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Chen, G., E, D. Support vector machines for cloud detection over ice-snow areas. Geo-spat. Inf. Sc. 10, 117–120 (2007). https://doi.org/10.1007/s11806-007-0047-7
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DOI: https://doi.org/10.1007/s11806-007-0047-7