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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65728

    Title: Analysis of the Performance of Different Classifiers for Cloud Detection Application
    Authors: 馬依霖;Maulidiyah,Nur
    Contributors: 資訊工程學系
    Keywords: 云检测;分类;支持向量机;随机森林;分类回归树;Cloud detection;Classifier;Support Vector Machine;Random Forest;Classification And Regression Tree
    Date: 2014-07-30
    Issue Date: 2014-10-15 17:09:01 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 云检测是一种提供必要的信息,如云层覆盖在许多应用中非常重要。的经典方法云检测是基于图像像素的红色和蓝色的比率的阈值。然而,很难选择合适的阈值对所有云的条件。另外,对于不同的全天空照相机所需的阈值是不同的。准确的云检测是一项具有挑战性的任务,因为云的颜色有时很容易地与天空或太阳的区域相混淆。
    ;Cloud detection is important for providing necessary information such as cloud cover in many applications. The classic method for cloud detection is based on thresholding of the red and blue ratio of an image pixel. However, it is difficult to select a suitable threshold for all cloud conditions. Also, the desired thresholds for different all-sky cameras are different. Accurate cloud detection is a challenging task since the colors of cloud sometimes easily be confused with sky or sun regions.
    In this thesis, we propose to perform cloud detection using supervised learning techniques. The cloud models are learned through the training process. There are many classifiers that can be used for this purpose. We consider popular classifiers including random forest, classification and regression tree, and support vector machine. We use the color information of a local image patch instead of using only one pixel value. The color values of the pixels in a local image patch are arranged as a feature vector. The results show that the cloud detection using the color information of a local image patch get better accuracy than using one pixel value. The results also show that the support vector machine (SVM) has the highest detection accuracy. To take advantage of the clues provided by multiple classifiers, we propose a voting process to combine multiple classifiers to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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