A method for cloud of classification of AVHRR image data with fractal dimension
Ryoichi Kawads and Mikio Takagi
Institute of Industrial Science
University of Tokyo, Tokyo, Japan
Abstract
The need for automated cloud detection and classification arises from the massive quantities of data by satellites. Only automated processing can handle this many data.
In this paper, we present a method for multi spectral cloud classification of Advanced Very high Resolution Radiometer (AVHRR) data, which uses fractal dimension for texture analysis a well as other features like channel-1 visible reflectivity, channel-4 infra-red brightness temperature, and so on.
Features to measure texture are important, especially for night-time analysis on infra-red data 9visibel feature are not available for night time analysis). These features provide some information for distinguishing cumuliform clouds from stratiform clouds or clear regions.
In order to represent different textures we calculated fractal dimension of each pixel in images, which was found to be more effective than local difference. Use of fractal dimension leads to correct interpretation of coat lines or tidal fronts, which are often mis-classified into clouds by use of difference.
The classification is based on maximum likelihood method, which uses features extracted for AVHRR data of NOAA (National Oceanic and Atmospheric Administration) satellites.
Introduction
In multispectral classification of satellite images, local texture such as variance, standard deviation, and difference is a very important feature especially at night. But using of these often lead to false classification of coast line and tidal fronts into clouds.
In this study we used fractal dimensions to represent different textures. Unlike other features described above, values of fractal dimension of border liens of different domains are not so large as that of clouds. Although the calculation time of fractal dimension is generally larger than that of others, we did pixel by the pixel considered -------------- that is much faster than calculation over a block whose center point is the pixel. Thus, we got good results almost free of the problems above.
The images used are map images made from Advanced Very high Resolution Radiometer's data of NOAA meteorological satellites. Channel-1 (0.58~0.68m m; visible) and 2 (0.725~1.1m m; middle IR), 4 (10.3~11.3mm ; far IR and 5(11.5~12.5m m : far IR) contains brightness temperature.
