Cloud classification of satellite image performed in two stages

To understand a comprehensive atmospheric state, it is important to classify clouds in satellite images into appropriate classes. Many researches utilizing various features concerning the cloud texture have been reported in cloud classification. However, some clouds can not be classified uniquely only with the texture features. According to the knowledge of the experts, they classify the clouds in two stages. They firstly categorize the clouds into the provisional classes according to the brightnesses of the satellite images. They then classify each provisional class into the objective class based on the texture, shape and velocity of the cloud employing the meteorological knowledge about the time and location of the image. In this paper, we propose a novel method for the cloud classification that consists of two stages and utilizes cloud movement as human experts adopt. We firstly classify the clouds into 20 classes based on their brightnesses of the two-band spectral images. We then closely analyze the classes according to five features such as the brightnesses, deviations of brightness and cloud velocity estimated by varying window size adaptively. The experimental results are shown to verify the proposed method.