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An object-oriented daytime land fog detection approach based on NDFI and fractal dimension using EOS/MODIS data.

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Bibliographic Details
Title: An object-oriented daytime land fog detection approach based on NDFI and fractal dimension using EOS/MODIS data.
Authors: Wen, Xiongfei1 (AUTHOR) wxfei19@gmail.com, Hu, Dunmei2 (AUTHOR), Dong, Xinyi3 (AUTHOR), Yu, Fan4 (AUTHOR), Tan, Debao1 (AUTHOR), Li, Zhe1 (AUTHOR), Liang, Yitong5 (AUTHOR), Xiang, Daxiang1 (AUTHOR), Shen, Shaohong1 (AUTHOR), Hu, Chengfang1 (AUTHOR), Cao, Bo1 (AUTHOR)
Source: International Journal of Remote Sensing. Jul2014, Vol. 35 Issue 13, p4865-4880. 16p.
Subjects: Earth Observing System (Program), MODIS (Spectroradiometer), Fractal dimensions, Fog control, Air quality, Remote sensing
Abstract: A new approach for land fog detection using daytime imagery from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data based on the normalized difference fog index (NDFI) is proposed. NDFI is used to discriminate fog from clouds based on simulating and analysing the radiation characteristics of fog and cloud with MODIS data and the Streamer radiative transfer model. In this paper, in addition to the spectral and spatial characteristics of NDFI, the textural characteristics are introduced by using a fractal dimension. The fractal dimension is calculated with a differential box-counting approach to differentiate the texture characteristics of cloud and fog, and then the spectral and texture features are combined using an NDFI weighted fractal dimension algorithm as a new feature to improve the existing daytime fog detection approach. The performance of this approach is evaluated against ground-based measurements over China in winter, and the approach is proved to be effective in detecting land fog accurately based on the three cases. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
Description
Abstract:A new approach for land fog detection using daytime imagery from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data based on the normalized difference fog index (NDFI) is proposed. NDFI is used to discriminate fog from clouds based on simulating and analysing the radiation characteristics of fog and cloud with MODIS data and the Streamer radiative transfer model. In this paper, in addition to the spectral and spatial characteristics of NDFI, the textural characteristics are introduced by using a fractal dimension. The fractal dimension is calculated with a differential box-counting approach to differentiate the texture characteristics of cloud and fog, and then the spectral and texture features are combined using an NDFI weighted fractal dimension algorithm as a new feature to improve the existing daytime fog detection approach. The performance of this approach is evaluated against ground-based measurements over China in winter, and the approach is proved to be effective in detecting land fog accurately based on the three cases. [ABSTRACT FROM PUBLISHER]
ISSN:01431161
DOI:10.1080/01431161.2014.930564