Cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-Pattern Analysis
Separation of clouds from snow and ice is fundamentally very challenging because these features display very similar spectral characteristics. To investigate how to improve the separation of these features using the fractal summation method and spatial point-pattern analysis, Landsat 8 Operational Land Imager (OLI) images containing both cloud and snow/ice were used as a data source. Selective principal component analysis (SPCA) was applied to those bands sensitive to information regarding cloud and snow, and the fractal summation method and spatial point-pattern analysis were then applied. This innovative cloud detection method was found to be an effective tool for reducing non-cloud false anomalies in images where snow and ice share similar spectra with clouds.
KeywordsRemote sensing Cloud detection Snow/Ice Fractal summation method Hotspot analysis
This work was financially supported by the project of open fund for key laboratory of land and resources degenerate and unused land remediation, under Grant [SXDJ2017-7], and the 1:50, 000 geological mapping in the loess covered region of the map sheets: Caobizhen (I48E008021), Liangting (I48E008022), Zhaoxian (I48E008023), Qianyang (I48E009021), Fengxiang (I48E009022), & Yaojiagou (I48E009023) in Shaanxi Province, China, under Grant [DD-20160060].
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