| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 102 === With the increasing importance of environment protection, there are more and more research works in analyzing the clouds to help the solar plant to realize the effect caused by the clouds after a period of time. Because the ability to block the Sun differs from different kinds of clouds, automatically classifying the clouds will help the solar plant to allocate or manage the power system.
However, there are some difficulties existing in cloud classification. The large variety within the same cloud type and the similarity between the different cloud types both make the task more challenging. Besides, the dramatic light change caused by the relative position of the Sun and clouds will make it more difficult.
To deal with the problems, we make efforts in extracting more powerful features to classify different cloud types. Besides, we propose a method which use "block" instead of whole image to classify the mixed conditions. In our research, we divide the whole image into multiple equal-sized blocks, then extract statistical and local texture features from them. Then we’ll train models using k-Nearest Neighbors and Support Vector Machine. Before we start classify, we use Solar Position Algorithm to skip the blocks where the Sun locates in. After classification of each block, we use voting mechanism to decide the result of the image. In our experiment, we found that the proposed method outperforms the method using only whole image.
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