CLOUD DETECTION FOR NIGHT-TIME PANCHROMATIC VISIBLE AND NEAR-INFRARED SATELLITE IMAGERY
| Autor: | Tobias Storch, L. Joachim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Applied optics. Photonics
010504 meteorology & atmospheric sciences 0211 other engineering and technologies Cloud computing 02 engineering and technology 01 natural sciences lcsh:Technology Satellite imagery 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Random Forest business.industry lcsh:T Near-infrared spectroscopy Moon Illumination lcsh:TA1501-1820 Snow Day-Night-Band Random forest VNIR Panchromatic film Night-Time Satellite Imagery lcsh:TA1-2040 Cloud albedo Environmental science business lcsh:Engineering (General). Civil engineering (General) Cloud Detection Urban Areas |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol V-2-2020, Pp 853-860 (2020) |
| ISSN: | 2194-9050 2194-9042 |
| Popis: | Cloud detection for night-time panchromatic visible and near-infrared (VNIR) satellite imagery is typically performed based on synchronized observations in the thermal infrared (TIR). To be independent of TIR and to improve existing algorithms, we realize and analyze cloud detection based on VNIR only, here NPP/VIIRS/DNB observations. Using Random Forest for classifying cloud vs. clear and focusing on urban areas, we illustrate the importance of features describing a) the scattering by clouds especially over urban areas with their inhomogeneous light emissions and b) the normalized differences between Earth’s surface and cloud albedo especially in presence of Moon illumination. The analyses substantiate the influences of a) the training site and scene selections and b) the consideration of single scene or multi-temporal scene features on the results for the test sites. As test sites, diverse urban areas and the challenging land covers ocean, desert, and snow are considered. Accuracies of up to 85% are achieved for urban test sites. |
| Databáze: | OpenAIRE |
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