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Band Selection Method Based on Spectrum Difference in Targets of Interest in Hyperspectral Imagery

机译:基于频谱差异的频谱差异在高光谱图像中的频谱差异

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While hyperspectral data shares rich spectrum information, it has numbers of bands with high correlation coefficients, causing great data redundancy. A reasonable band selection is important for subsequent processing. Bands with large amount of information and low correlation should be selected. On this basis, according to the needs of target detection applications, the spectral characteristics of the objects of interest are taken into consideration in this paper, and a new method based on spectrum difference is proposed. Firstly, according to the spectrum differences of targets of interest, a difference matrix which represents the different spectral reflectance of different targets in different bands is structured. By setting a threshold, the bands satisfying the conditions would be left, constituting a subset of bands. Then, the correlation coefficients between bands are calculated and correlation matrix is given. According to the size of the correlation coefficient, the bands can be set into several groups. At last, the conception of normalized variance is used on behalf of the information content of each band. The bands are sorted by the value of its normalized variance. Set needing number of bands, and the optimum band combination solution can be get by these three steps. This method retains the greatest degree of difference between the target of interest and is easy to achieve by computer automatically. Besides, false color image synthesis experiment is carried out using the bands selected by this method as well as other 3 methods to show the performance of method in this paper.
机译:虽然高光谱数据共享丰富的频谱信息,但它具有具有高相关系数的频带数,导致具有很大的数据冗余。合理的频带选择对于后续处理很重要。应选择具有大量信息和低相关的频段。在此基础上,根据目标检测应用的需要,在本文中考虑了感兴趣对象的光谱特性,提出了一种基于频谱差的新方法。首先,根据感兴趣的目标的频谱差异,表示不同频带中不同靶的不同谱反射的差分矩阵是结构化的。通过设置阈值,将留下满足条件的频带,构成频带子集。然后,计算频带之间的相关系数,并给出相关矩阵。根据相关系数的大小,可以将频段设置为几组。最后,代表每个频带的信息内容使用归一化方差的概念。频段按其归一化方差的值进行排序。设置需要频带数,最佳频带组合解决方案可以通过这三个步骤获得。该方法保留了感兴趣目标之间的最大程度的差异,并且易于通过计算机自动实现。此外,使用该方法选择的条带以及其他3种方法进行假彩色图像合成实验,以显示本文的方法的性能。

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