Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user’s accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user’s accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.

中文翻译：

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迭代训练样本扩展以通过VHR图像增加和平衡土地分类的准确性

已知不平衡的训练集会产生用于监督分类的次优图。因此，在绘制土地覆盖图时面临的一个挑战是获取训练数据，该数据将允许以较高的总体准确性（OA）进行分类，其中每个类别也都将映射到相似的用户准确性上。为了解决这个问题，我们在本文中将局部自适应区域和盒须图（BP）技术集成到一个迭代算法中，以扩展所选​​课程的训练样本的大小。所提出算法的主要步骤如下。首先，手动标记每个班级的非常小的初始训练样本（ITS）。其次，通过进行局部频谱变化分析，在自适应区域内找到潜在的新训练样本。最后，获取三个新的训练样本以捕获有关类内变异的信息；这些样本位于BP的较低，中位数和较高四分位数中。在将这些新的训练样本添加到ITS之后，对分类进行重新训练，并且该过程将反复进行，直到终止。所提出的方法已应用于三个非常高分辨率（VHR）的遥感图像，并与一组同类方法进行了比较。比较表明，该方法在OA方面取得了最佳效果，并且在平衡用户准确性方面表现出优势。例如，就OA而言，所提出的方法通常比所比较的方法准确2％-10％，并且通常产生最平衡的分类。在将这些新的训练样本添加到ITS之后，对分类进行重新训练，并且该过程将反复进行，直到终止。所提出的方法已应用于三个非常高分辨率（VHR）的遥感图像，并与一组同类方法进行了比较。比较表明，该方法在OA方面取得了最佳效果，并且在平衡用户准确性方面表现出优势。例如，就OA而言，所提出的方法通常比所比较的方法准确2％-10％，并且通常产生最平衡的分类。在将这些新的训练样本添加到ITS之后，对分类进行重新训练，并且该过程将反复进行，直到终止。所提出的方法已应用于三个非常高分辨率（VHR）的遥感图像，并与一组同类方法进行了比较。比较表明，该方法在OA方面取得了最佳效果，并且在平衡用户准确性方面表现出优势。例如，就OA而言，所提出的方法通常比所比较的方法准确2％-10％，并且通常产生最平衡的分类。比较表明，该方法在OA方面取得了最佳效果，并且在平衡用户准确性方面表现出优势。例如，就OA而言，所提出的方法通常比所比较的方法准确2％-10％，并且通常产生最平衡的分类。比较表明，该方法在OA方面取得了最佳效果，并且在平衡用户准确性方面表现出优势。例如，就OA而言，所提出的方法通常比所比较的方法准确2％-10％，并且通常产生最平衡的分类。