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Algorithm for classification of multispectral data and its implementation on a massively parallel computer

机译:多光谱数据分类算法及其在大规模并行计算机上的实现

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Abstract: A new method for classification of multi-spectral data is proposed. This method is based on fitting mixtures of multivariate Gaussian components to training and unlabeled samples by using the EM algorithm. Through a backtracking search strategy with appropriate depth bounds, a series of mixture models are compared. The validity of the candidate models are evaluated by considering their description lengths and allocation rates. The most suitable model is selected and the multi-spectral data are classified accordingly. The EM algorithm is mapped onto a massively parallel computer system to reduce the computational cost. Experimental results show that the proposed algorithm is more robust against variations in training samples than the conventional supervised Gaussian maximum likelihood classifier.!11
机译:摘要:提出了一种新的多光谱数据分类方法。该方法基于通过使用EM算法将多元高斯分量的混合物拟合到训练样本和未标记样本的方法。通过具有适当深度范围的回溯搜索策略,比较了一系列混合模型。通过考虑候选模型的描述长度和分配率来评估其有效性。选择最合适的模型,然后对多光谱数据进行分类。 EM算法被映射到大规模并行计算机系统上以减少计算成本。实验结果表明,与传统的监督式高斯最大似然分类器相比,所提出的算法对训练样本变化的鲁棒性更强。11

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