## 4 Discussion
### Novelty of the Study and Implications for Training Sample Selection
The sample impurity corresponded to class purity in this study, and the landscape heterogeneity was opposite to the GCH defined in this study. In particular, when the landscape heterogeneity was high, the corresponding classification accuracy decreased. The Illinois region classified in this study also exhibited high class heterogeneity. However, the classification accuracy was improved significantly when small size training patches with low class purity were used for classification. Therefore, training samples in patch-based classification should be collected byconsidering both characteristics of the study area (class purity and landscape homogeneity) and the classification parameter (patch size in CNN-based classification). The spatial resolution of remote sensing images used for classification is also one of the important factors for the collection of training samples in supervised classification. Many studies have demonstrated that landscape heterogeneity and training sample size might vary according to the spatial resolution [21; 39; 45]. In this study, the LCH was defined to properly account for the difference in spatial resolution of input images in two classification regions (0.25 m vs. 30 m). The \(P_{i}\) value in Equation (1) can vary with respect to the spatial resolution of the input images. This indicates that the strategy for training sample collection based on class purity should be changed because LCH values may change accordingly even in the same region when remote sensing images with different spatial resolution are used. For example, when coarser spatial resolution images are used for classification in Anbandegi, the LCH values will decrease accordingly; hence, a different reference class purity value should be determined. Furthermore, the spatial resolution affects the quantity of the training patches. If a high class purity value is selected for the classification with coarse spatial resolution images, only a small number of training patches may be collected, similar to the Illinois case. Therefore, the class purity, LCH, and GCH defined in this study should be used to collect training patches according to the spatial resolution of the input images. Deep learning-based classifiers require the sufficient amount of training samples to achieve satisfactory classification performance [28; 29]. If such a small number of training patches are used for CNN-based classification, it may be difficult to seek the optimal parameters of deep neutral network structures, thereby yielding poor classification performance. In addition to the quantity of training samples, the quality of training samples is important in supervised classification because the learning process is based on the representativeness of the training samples for the classes of interest. In particular, the classification results of deep learning models depend on high-level features extracted from representation learning with training patches [46; 47]. Therefore, the information content contained in training patches is critical for extracting informative spatial features. As discussed, using training patches with different class purity values and patch sizes produces spatial features with different characteristics. Hence, training patches with a specific class purity and patch size based on class homogeneity in the study area should be selected to discriminate homogeneous subareas (inside the parcels) from heterogeneous ones (boundaries between parcels) and vice versa.