Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set of training samples may produce a different classification result. A general approach to addressing this problem is the so-called K-fold method which implements RTS K times and takes the average of overall accuracy with respect to standard deviation to describe a confidence level of classification performance. To deal with this issue, this article develops an iterative RTS (IRTS) method as an alternative to the K-fold method to reduce the uncertainty caused by RTS. Its idea is to add the spatial filtered classification maps to the image cube that is currently being processed via feedback loops to augment image cubes iteratively. Then, the training samples will be reselected randomly from the new augmented image cubes iteration-by-iteration. As a result, the training samples selected from each iteration will be updated by new added spatial information captured by spatial filters implemented at the iteration. The experimental results clearly demonstrate that IRTS successfully improves classification accuracy as well as reduces inconsistency in results.

中文翻译：

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高光谱图像的迭代随机训练采样光谱空间分类

高光谱图像分类（HSIC）在过去几年引起了相当大的兴趣。然而，HSIC 中出现的一个具有挑战性的问题是分类不一致，这主要是由选择训练数据的随机训练抽样 (RTS) 引起的。这是因为不同的训练样本集可能会产生不同的分类结果。解决这个问题的一般方法是所谓的 K-fold 方法，它实现 RTS K 次并取相对于标准偏差的整体准确度的平均值来描述分类性能的置信水平。为了解决这个问题，本文开发了一种迭代RTS（IRTS）方法作为K-fold方法的替代方法，以减少RTS引起的不确定性。它的想法是将空间过滤的分类图添加到当前通过反馈循环处理的图像立方体中，以迭代地增强图像立方体。然后，将从新的增强图像立方体中逐次迭代地随机重新选择训练样本。因此，从每次迭代中选择的训练样本将通过在迭代中实现的空间过滤器捕获的新添加的空间信息进行更新。实验结果清楚地表明，IRTS 成功地提高了分类精度并减少了结果的不一致。从每次迭代中选择的训练样本将通过在迭代中实现的空间过滤器捕获的新添加的空间信息进行更新。实验结果清楚地表明，IRTS 成功地提高了分类精度并减少了结果的不一致。从每次迭代中选择的训练样本将通过在迭代中实现的空间过滤器捕获的新添加的空间信息进行更新。实验结果清楚地表明，IRTS 成功地提高了分类精度并减少了结果的不一致。