The extensive amount of Earth observation satellite images available brings opportunities and challenges for land mapping in global and regional scales. These large datasets have motivated the use of satellite image time series analysis coupled with machine learning techniques to produce land use and cover class maps. To be successful, these methods need good quality training samples, which are the most important factor for determining the accuracy of the results. For this reason, training samples need methods for quality control of class noise. In this paper, we propose a method to assess and improve the quality of satellite image time series training data. The method uses self-organizing maps (SOM) to produce clusters of time series and Bayesian inference to assess intra-cluster and inter-cluster similarity. Consistent samples of a class will be part of a neighborhood of clusters in the SOM map. Noisy samples will appear as outliers in the SOM. Using Bayesian inference in the SOM neighborhoods, we can infer which samples are noisy. To illustrate the methods, we present a case study in a large training set of land use and cover classes in the Cerrado biome, Brazil. The results prove that the method is efficient to reduce class noise and to assess the spatio-temporal variation of satellite image time series training samples.

现有的大量地球观测卫星图像为全球和区域尺度的土地制图带来了机遇和挑战。这些大型数据集激发了卫星图像时间序列分析与机器学习技术的结合，以产生土地利用和覆盖物类图。为了获得成功，这些方法需要高质量的训练样本，这是确定结果准确性的最重要因素。因此，训练样本需要用于分类噪声质量控制的方法。在本文中，我们提出了一种评估和改善卫星图像时间序列训练数据质量的方法。该方法使用自组织映射（SOM）生成时间序列的集群，并使用贝叶斯推断来评估集群内和集群间的相似性。一个类的一致样本将成为SOM映射中聚类附近的一部分。嘈杂的样本将在SOM中显示为异常值。使用SOM邻域中的贝叶斯推断，我们可以推断出哪些样本有噪声。为了说明这些方法，我们在巴西Cerrado生物群落的大量土地使用和覆盖课程培训中提供了一个案例研究。结果证明，该方法可有效降低类噪声并评估卫星图像时间序列训练样本的时空变化。