## 4 Discussion  
### Novelty of the Study and Implications for Training Sample Selection  
Previous studies for supervised classification emphasized the importance of collecting informative training samples and investigated the effects of different strategies in selecting training samples on classification performance [9; 10; 11; 12; 14; 21]. However, the strategies on selecting informative training samples were developed for pixel-based classification. Because training patches that contain neighboring pixels are used in patch-based classification, classification performance is affected by both the representativeness of a training patch for a specific class and the degree of compositional homogeneity of classes within a training patch. These two factors are related to landscape heterogeneity of the study area [21; 42; 43]. [PERSON] et al. [42; 43] reported that classification accuracy decreased as land-cover heterogeneity increased and patch size decreased in USA. However, the evaluation was based on the comparison between classification accuracy and landscape variables, not on classification results. In patch-based classification, [PERSON] et al. [30] used training samples that have a class purity value of more than 60% for CNN-based classification without comparison with other class purity values. [PERSON] et al. [31] quantified the degree of class heterogeneity and then analyzed its effects on classification accuracy of patch-based CNN. An experiment on the classification of Landsat imagery revealed that moderately heterogeneous samples produced the highest accuracy. However, landscape heterogeneity was only used to demonstrate the superiority of patch-based CNN over pixel-based classifier, not to explicitly relate the global or local characteristics of landscape heterogeneity in the study area to classification performance. In contrast to previous studies, the novelty of this study lies in the explicit quantification of the effect of class purity of a training patch on patch-based crop classification through comparative experiments conducted in two regions with different landscape characteristics and input images. Crop classification experiments demonstrated that the class purity within a training patch significantly affected the classification results, including the accuracy and spatial distributions because the information content provided by patch-based training samples varies according to the diversity of class composition in a patch. The relatively large crop parcel or the inside of the parcel (homogeneous subareas) was correctly classified using training patches with high class purity. By contrast, training patches with low class purity could discriminate both adjacent crops and the crop type near boundaries (heterogeneous subareas) more successfully. This effectiveness of training patches with low class purity for the heterogeneous distributions of land-cover class is similar to the results from previous pixel-based classification studies that emphasized the necessity of including training data that lie close to the location of boundaries in a geographic [9] or feature space [44]. This study also newly defined the GCH and CV of the LCH that can be useful quantitative measures for the GCH and degree of relative variations of class homogeneity across the study area, respectively. These quantitative indices were used for the characterization of class homogeneity of the study area and the interpretations of classification results. If such summary statistics in the study area can be combined with the LCH, it may be possible to pinpoint the locations of informative training samples that are specific to the discrimination of subareas with different local homogeneity. It should be noted that class purity is inter-related to other factors, such as landscape heterogeneity, training patch size, and spatial resolution of input images. [PERSON] et al. [22] reported that both sample impurity and landscape heterogeneity affect the classification accuracy in pixel-based classification with coarse resolution MODIS images.