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首页> 外文期刊>IEEE Transactions on Neural Networks >Temporal updating scheme for probabilistic neural network with application to satellite cloud classification
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Temporal updating scheme for probabilistic neural network with application to satellite cloud classification

机译：概率神经网络的时间更新方案及其在卫星云分类中的应用

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摘要

In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual information and adjusting the PNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the PNN updated up to the last frame while at the same time, a prediction using Markov chain models is also made based on the classification results of the previous frame. The results of both the old PNN and the predictor are then compared. Depending on the outcome, either a supervised or an unsupervised updating scheme is used to update the PNN classifier. Maximum likelihood (ML) criterion is adopted in both the training and updating schemes. The proposed scheme is examined on both a simulated data set and the Geostationary Operational Environmental Satellite (GOES) 8 satellite cloud imagery data. These results indicate the improvements in the classification accuracy when the proposed scheme is used.
机译：在从卫星图像进行云分类时，图像中的时间变化是导致分类器性能下降的主要因素之一。在本文中，为概率神经网络（PNN）分类器开发了一种新颖的时间更新方法，该分类器可用于跟踪图像序列中的时间变化。这是通过利用时间上下文信息并调整PNN以适应此类更改来完成的。每当有一组新的图像到达时，首先使用更新到最后一帧的PNN进行初始分类，同时，还基于前一帧的分类结果使用马尔可夫链模型进行预测。然后比较旧的PNN和预测器的结果。根据结果​​，可以使用有监督的更新方案或无监督的更新方案来更新PNN分类器。训练和更新方案均采用最大似然（ML）准则。在模拟数据集和对地静止作战环境卫星（GOES）8卫星云影像数据上都检查了提出的方案。这些结果表明使用所提出的方案时分类精度的提高。

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