Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 32, Issue 17, Pages 4769-4785Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2010.489067
Keywords
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Funding
- National Science & Technology Pillar Program of China [2008BAK49B07]
- National Natural Science Foundation of China [40901208, 40901211]
- MODIS Satellite Data Reception Station at Wuhan University
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A new algorithm is presented for land-fog detection using daytime imagery from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data. MODIS images constitute an ideal data source for fog detection due to their outstanding spatial and spectral resolution. In this article, a parameter named the Normalized Difference Fog Index (NDFI) is proposed, based on analysing the spectral character of fog and cloud by utilizing the Streamer radiativetransfer model and MODIS data. A mean-shift segmentation method is used to preliminary segment the NDFI image, and a full lambda-schedule algorithm is then iteratively applied to merge adjacent segments based on the combination of spectral and spatial information. Then, some properties (e. g. mean value of brightness temperature) are calculated for each segment, and each object is identified as either fog or not. The algorithm's performance is evaluated against ground-based measurements over China in winter, and the algorithm is proved to be effective in detecting fog accurately based on three cases.
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