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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Multiclass and Binary SVM Classification: Implications for Training and Classification Users
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Multiclass and Binary SVM Classification: Implications for Training and Classification Users

机译：多类和二进制SVM分类：对培训和分类用户的启示

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Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multiclass classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00%) than the approaches based on a series of binary classifications (89.22% and 91.33%). Additionally, the one-shot multiclass SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters $C$ and $gamma$ as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method.
机译：支持向量机（SVM）在监督分类分析方面具有相当大的潜力，但其二进制性质一直限制了它们在遥感中的使用。这通常需要遵循一种对一种策略或一种对所有策略，将多类分析分解为一系列二进制分类。但是，可以将二进制SVM扩展为需要一次优化操作的单发多类分类。在这里，针对基于五类分类的二进制SVM方法，对多光谱数据的单次多类分类方法进行了评估。与基于一系列二进制分类的方法（89.22％和91.33％）相比，一次性多分类法更准确（92.00％）。此外，与基于二进制SVM的方法相比，单次多类SVM具有其他优点，尤其是对于参数$ C $和$ gamma $仅需要优化一次，而针对所有参数和参数，仅需要优化五次。一对一方法分别为10倍，并且使用的支持向量更少，而基于二进制的方法则为243和246。在对较大维度的数据集进行分析的结果中，类似的趋势也很明显。同样很明显，不能保证传统的“一反所有”策略会产生一个完整的混淆矩阵，该矩阵会极大地限制评估和后来使用该方法得出的分类。

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