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Classification of Multi-spectral/Hyperspectral Data using Genetic Programming and Error-correcting Output Codes

机译:使用遗传编程和纠错输出代码对多光谱/高光谱数据进行分类

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Genetic programming (GP) and error-correcting output codes (ECOC) are combined to develop a new classification method (GP-ECOC) for the multi-class problem solving in this paper. Some additional improvements on the algorithm, modified codeword matrix and group division before classification, are also proposed to settle several existing problems in multi-spectral and hyperspectral data classification. Experimental tests using both multi-spectral and hyperspectral data are carried out for verification and illustration. It is observed from the obtained results that the classification precision with the newly proposed method is greatly enhanced compared with some existing methods using GP, and the proposed improvements are also effective. The algorithm of GP-ECOC and its improved versions can also be run on multi-terminals, which saves computational cost effectively
机译:结合遗传编程(GP)和纠错输出代码(ECOC)以开发本文中的多级问题的新分类方法(GP-ECOC)。还提出了对分类之前的算法,修改的码字矩阵和组分部的一些额外改进,以解决多光谱和高光谱数据分类中的几个存在问题。使用多光谱和高光谱数据进行实验测试进行验证和图示。从所得结果观察到,与使用GP的一些现有方法相比,具有新提出的方法的分类精度大大提高,并且提出的改进也是有效的。 GP-ECOC及其改进版本的算法也可以在多终端上运行,从而节省有效的计算成本

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