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Correlated probabilities based decision fusion method for multi-sensor data

机译：基于相关概率的多传感器数据决策融合方法

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Most of the decision fusion techniques developed for the remote sensing applications have the drawback of assuming the conditional independence between the classification results, whereas, usually the correlation exists due to the same measuring instrument or same area under study. Fusion of Correlated Probabilities (FCP) method has a potential to deal with conditional dependence only for two data sets. The proposed Multi-sensor Fusion of Correlated Probabilities (MFCP) algorithm is the extended version of FCP by modifying the cross conditional dependence between three or more multi-sensor information sources. The proposed MFCP method is assessed in the multi-sensor land cover classification over the Beijing area for three sensor data sets. We evaluated and validated our proposed methodology by comparing it with four existing fusion methods. The experimental results demonstrated that the proposed MFCP method outperformed all the compared fusion methods in terms of overall accuracy, kappa and class wise accuracies. Therefore, the MFCP method is adaptable to any type and any number of sensor data sets.
机译：为遥感应用开发的大多数决策融合技术都有缺点，即假设分类结果之间存在条件独立性，而通常由于相同的测量仪器或相同的研究区域而存在相关性。相关概率融合（FCP）方法可能仅处理两个数据集的条件相关性。所提出的相关概率的多传感器融合（MFCP）算法是FCP的扩展版本，它通过修改三个或更多多传感器信息源之间的交叉条件依赖性来实现。在北京地区的多传感器土地覆盖分类中，针对三个传感器数据集评估了所提出的MFCP方法。通过与四种现有融合方法进行比较，我们评估并验证了我们提出的方法。实验结果表明，所提出的MFCP方法在整体精度，kappa和逐级精度方面均优于所有比较的融合方法。因此，MFCP方法适用于任何类型和数量的传感器数据集。

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