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首页> 外文会议>2011 IEEE International Geoscience Remote Sensing Symposium >Techniques based on Support Vector Machines for cloud detection on QuickBird satellite imagery
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Techniques based on Support Vector Machines for cloud detection on QuickBird satellite imagery

机译：基于支持向量机的QuickBird卫星图像云检测技术

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Purpose of this work is the study of cloud detection techniques. This work identifies the cloud cover of optical images acquired by the QuickBird satellite, comparing these with others of the same area, acquired by Landsat 7 in which there are no clouds. The images are combined using an early fusion technique [1]. The tool exploits the neighborhood model [2] for increasing the amount of information for the training set and the Singular Value Decomposition for carrying out the feature extraction [3]. In order to introduce these structures into thematic classification tasks by SVMs it was necessary develop a tree kernel function based on tree kernel function defined in SVM-LightTK. The aim of the tree kernel function is evaluate the similarity level between a generic couples of tree structures. In this paper we report the results obtained comparing the performance of different approaches in cloud classification problem. The final purpose is the production of cloud cover maps. Throughout such different experimental setups we measured the capabilities of each algorithm under different points of view. First of all, we considered the classification accuracy by computing traditional parameter such as overall accuracy. A second analysis regarded the efforts that are required in the design of optimal algorithms. Indeed, these techniques are characterized by different parameters that have to be appropriately tuned in order to obtain the best performance. Finally the robustness of the techniques has been also considered. In particular the classification accuracy has been evaluated also for images not considered in the training phase.
机译：这项工作的目的是研究云检测技术。这项工作确定了QuickBird卫星采集的光学图像的云层，并将其与Landsat 7采集的没有云的相同区域的其他图像进行了比较。使用早期融合技术将图像合并[1]。该工具利用邻域模型[2]来增加训练集的信息量，并利用奇异值分解来进行特征提取[3]。为了通过SVM将这些结构引入到主题分类任务中，有必要基于SVM-LightTK中定义的树核函数来开发树核函数。树内核功能的目的是评估通用的几对树结构之间的相似度。在本文中，我们报告了比较云分类问题中不同方法的性能所获得的结果。最终目的是制作云量图。在不同的实验设置中，我们从不同的角度测量了每种算法的功能。首先，我们通过计算传统参数（例如整体精度）来考虑分类精度。第二次分析考虑了优化算法设计中需要付出的努力。实际上，这些技术的特征在于必须适当调整不同参数才能获得最佳性能。最后，还考虑了该技术的鲁棒性。特别地，还针对训练阶段中未考虑的图像评估了分类精度。

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