Nconsistency between the prediction and observation. MCC can be calculated as follows: MCC =Remote Sens. 2021, 13, x FOR PEER Review( TP+ FP) TP+ FN )+( TN + FN ) TN + FP)TP TN – FP FN( TP + FP) ( TP + FN ) ( TN + FN ) ( TN + FP)(10)20 ofBoth MCC and Kappa can be applied to evaluate the classification accuracy of unbalanced datasets, while some researchers believe that MCC is far better than the Kappa coefficient [51]. Hence, 0.8200indicators have been utilized to 1778 both analyze the results. All 3 23 0.9329 0.8315 1352 indicators of every tree have been 0.7913 calculated and are listed in Table five. As shown, the OA values 24 0.9365 0.8065 938 1216 of all 24 trees are very high, 0.8547 from 0.8627 to 0.9872, plus the average OA value was ranging 0.9167 Imply 0.9550 / 1423 0.9550; the Kappa coefficients ranged from 0.7276 to 0.9191, and also the average value was 412 0.8547; the MCC values ranged from 0.7544 to 0.9211, and the typical worth 12. Clearly, Within the OA, Kappa, and MCC values of each and every tree are also plotted in Figure was 0.8627. 413 Table five, there is just about no difference betweengiven byand MCC values. that provided by 414 the overall classification accuracy evaluation Kappa OA is greater than The OA, Kappa, plotted Kappa and each tree are also plotted same. The OA values 415 Kappa and MCC. Theand MCC values ofMCC values are MitoBloCK-6 medchemexpress pretty much thein Figure 12. Clearly, the overall classification accuracy evaluation givenalthough their Kappa values are BMY 7378 Description smaller 416 of trees four, 12, 13, 22, and 24 are larger than 0.9, by OA is greater than that provided by Kappa and MCC. The plotted Kappa and MCC values are practically the exact same. The OA values of trees than 0.8. 417 4, 12, 13, 22, and 24 are bigger than 0.9, despite the fact that their Kappa values are smaller sized than 0.8.Figure 12. The histogram OA, Kappa, and MCC of 24 trees’ classification benefits. Figure 12. The histogram of OA, Kappa, and MCC of 24 trees’ classification outcomes.419 420 421In terms of processing speed evaluation, the time price of each and every tree is reported in Table five. Due to the unique numbers of tree points, the time expenses per million points had been also calculated and are detailed in Table 5. Usually, the a lot more points that exist, the more timeRemote Sens. 2021, 13,19 ofTable five. The accuracy and efficiency analysis of 24 trees classification final results. Accuracy Analysis Tree/Number OA 1 two 3 4 5 6 7 eight 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Imply 0.9739 0.9631 0.9582 0.9279 0.9580 0.9647 0.9740 0.9603 0.9167 0.9669 0.9579 0.9267 0.9776 0.9633 0.9792 0.9872 0.9624 0.9316 0.9575 0.9475 0.9683 0.9295 0.9329 0.9365 0.9550 Kappa 0.9032 0.8870 0.8979 0.7726 0.9113 0.9027 0.9191 0.8952 0.8076 0.8219 0.9130 0.7923 0.7837 0.8995 0.8762 0.9080 0.8080 0.8164 0.8872 0.8910 0.8805 0.7276 0.8200 0.7913 0.8547 MCC 0.9066 0.8889 0.9012 0.7923 0.9144 0.9061 0.9211 0.8983 0.8203 0.8331 0.9162 0.8080 0.8021 0.9024 0.8808 0.9116 0.8115 0.8281 0.8919 0.8949 0.8843 0.7544 0.8315 0.8065 0.8627 Time Evaluation Time Price (ms) 935 930 870 912 1901 1350 5547 1565 1625 1103 2456 917 506 2981 990 880 791 1789 12753 3517 1334 1392 1778 938 / TPMP (ms) 1067 1298 1383 1244 1786 1390 1633 1347 1521 912 1863 1236 2489 1572 917 898 940 1319 2590 2049 1046 1070 1352 1216In terms of processing speed analysis, the time price of every tree is reported in Table 5. Because of the unique numbers of tree points, the time expenses per million points had been also calculated and are detailed in Table 5. Frequently, the a lot more points that exist, the extra time the processing takes. As.