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Support vector classification of remote sensing images using improved spectral Kernels

机译:使用改进的光谱核支持遥感图像的矢量分类

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摘要

A very important task in pattern recognition is the incorporation of prior information into the learning algorithm. In SUppOlt vector machines this task is performed via the kernel function. Thus for each application if the right kernel function is chosen, the amount of prior information fed into the machine is increased and thus the machine will perform with much more functionality. In the case of hyper-spectral imagery the amount of information available prior to classification is a vast amount. Current available kernels do not take full advantage of the amount of information available in these images. This paper focuses on deriving a set of kernels specific to these imagery. These kernels make use of the spectral signature available in images. Subsequently we use mixtures of these kernels to derive new and more efficient kernels for classification. Results show that these kernels do in fact improve classification accuracy and use the prior information available in imagery to a better degree.
机译:模式识别中非常重要的任务是将先验信息整合到学习算法中。在SUppOlt向量机中,此任务是通过内核功能执行的。因此,对于每个应用程序,如果选择了正确的内核功能,则送入机器的先验信息量将增加,因此机器将以更多的功能运行。在高光谱图像的情况下,分类之前可用的信息量很大。当前可用的内核没有充分利用这些映像中可用的信息量。本文着重于推导一组特定于这些图像的内核。这些内核利用图像中可用的光谱特征。随后,我们使用这些内核的混合来获得新的和更有效的内核进行分类。结果表明,这些内核实际上确实提高了分类准确性,并更好地使用了图像中的现有信息。

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