As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area.

中文翻译：

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卷积神经网络训练斑块纯度对农作物分类性能的影响

由于使用卷积神经网络（CNN）进行监督分类的性能会受到训练补丁的显着影响，因此有必要在基于补丁的分类中分析训练补丁的信息内容的影响。这项研究的目的是定量研究训练补丁的分类纯度对作物分类性能的影响。在此，将类别纯度（指的是训练补丁中类别的组成均匀性的程度）视为量化由训练补丁传达的信息的主要因素。提出了研究区域内类同质性和局部类同质性变化的新定量指标，以表征研究区域的空间同质性。在两个区域（韩国的Anbandegi和美国的伊利诺伊州）进行了使用2D-CNN的农作物分类，该农作物具有独特的农作物空间分布和该地区的阶级同质性，以突出训练斑块的阶级纯度的影响。在具有高度同质性的Anbandegi地区，当使用具有高纯度的大尺寸训练贴片时，可以获得更高的分类准确度（与最小的贴片尺寸和最低的纯度相比，总体准确度提高了7.1％p）。具有高纯度的训练斑块可以更好地识别同质农作物。相比之下，使用具有低分类纯度的小尺寸训练斑块在伊利诺伊州具有低分类均质性的地区获得最高的分类精度（19。与最大分类色块尺寸和最高纯度的分类相比，总体准确度提高了8％p）。具有低纯度的训练斑块可为鉴定各种农作物包裹提供有用的信息。结果表明，应基于反映研究区域局部班级同质性的班级纯度，选择基于补丁的分类中的训练样本。