## 5 Conclusions  
Previous studies have tested the effects of training samples on classification accuracy in supervised classification, but a comparative analysis of training sample selection considering variations of class purity within a training patch has not yet been fully performed in patch-based classification. This analysis is important in patch-based classification because classification accuracy is sensitive to the information content of training patches. In this study, the effects of class purity in a training patch on the performance of crop classification using a 2D-CNN were investigated through crop classification experiments conducted in two regions with different spatial homogeneity. Two quantitative measures were also used to quantify global class homogeneity of the study area and local variations of class homogeneity across the study area. Crop classification experiments conducted in two regions with different class compositions and spatial homogeneity demonstrated that the class purity of a training patch and patch size had significant influence on the classification accuracy, depending on the class homogeneity of the study area. For the classification of regions with high class homogeneity using high resolution remote sensing imagery, using large training patches with high class purity is beneficial. Meanwhile, small training patches with low class purity are effective for the classification of regions with high class heterogeneity using coarse resolution remote sensing imagery. These results indicate that the classification accuracy of patch-based classification can be improved using informative training samples selected by considering both class purity and spatial homogeneity of the study area. Therefore, the key findings of this study are expected to serve as a useful basis for selecting training samples in patch-based supervised classification; however, to expand the applicability of the major findings of this study, other important issues not investigated in this study, including consideration of spectral purity with class purity and selection of an appropriate threshold value of class purity, should be investigated through extensive experiments. The following are available online at [[http://www.mdpi.com/2076-3417/10/11/3773/s1](http://www.mdpi.com/2076-3417/10/11/3773/s1)]([http://www.mdpi.com/2076-3417/10/11/3773/s1](http://www.mdpi.com/2076-3417/10/11/3773/s1)), Figure S1: Classification results generated using other patch sizes and different class purity values in Anbandegi: (a) 5 by 5 patch; (b) 13 by 13 patch, (c) 15 by 15 patch; and (d) 21 by 21 patch. Each map is one of four classification results with the highest overall accuracy. Figure S2: Classification results generated using other patch sizes and different class purity values in Illinois: (a) 5 by 5 patch; (b) 9 by 9 patch; and (c) 15 by 15 patch. Each map is one of four classification results with the highest overall accuracy. Table S1: Accuracy statistics of the classification results using other patch sizes and different class purity values in Anbandegi (PA: producer's accuracy; UA: user's accuracy; OA: overall accuracy). Classification accuracy values from one of four classification results with the highest overall accuracy are displayed. Table S2: Accuracy statistics of the classification results using other patch sizes and different class purity values in Illinois (PA: producer's accuracy; UA: user's accuracy; OA: overall accuracy). Classification accuracy values from one of four classification results with the highest overall accuracy are displayed. Conceptualization, S.P. and N.-W.P. ; methodology, S.P. and N.-W.P. ; software, S.P. ; validation, S.P. ; formal analysis, S.P. ; investigation, S.P. and N.-W.P. ; writing--original draft preparation, S.P. ; writing--review and editing, N.-W.P. ; supervision, N.-W.P. ; Funding acquisition, N.-W.P. All authors have read and agreed to the published version of the manuscript. This work was supported by INHA UNIVERSITY Research Grant.