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Support Vector Machine and various methods of Multi-Spectral satellite image classification

机译:支持向量机和多光谱卫星图像分类的各种方法

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摘要

Use of satellite images is one of the prominent methods for information about land coverage. Multi-Spectral satellite image is an appropriate source for providing this information. Classification of these Multi-Spectral images is an effective way to recover the information. This can be achieved based on the kinds of pattern models used, the types of information used, the manner in which they are applied to the image and the manner in which they partition the image into classes. Here, along with Support Vector Machine (SVM) algorithm, various other classification techniques are discussed and compared based on several parameters.
机译:卫星图像的使用是获取土地覆盖信息的主要方法之一。多光谱卫星图像是提供此信息的合适来源。这些多光谱图像的分类是恢复信息的有效方法。这可以基于所使用的模式模型的种类,所使用的信息的类型,将其应用于图像的方式以及将图像划分为类别的方式来实现。在此,与支持向量机(SVM)算法一起,讨论了多种其他分类技术,并基于几个参数进行了比较。

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