Accuracy final results obtained by distinct classification procedures for unique data sets are shown in Tables four and Figures 91. The OAs obtained by the ML, MD, and SVM methods for the GF-3 information are 52.five , 55.7 , and 80.7 , and the Kappa coefficients are 0.36, 0.41, and 0.70, respectively. The above classification accuracy may be the lowest of all classification processes, possibly on account of common BSJ-01-175 Cancer wetland structural situations. In contrast, the OAs together with the ML, MD, and SVM strategies for the OHS data are 96.7 , 87.six , and 95.six , as well as the Kappa coefficients are 0.95, 0.82, and 0.94, respectively. This may perhaps be attributed to the increased spectral separation capacity of the biochemical traits of wetland sorts. GS-626510 Epigenetic Reader Domain Subsequently, the classification accuracy immediately after information fusion is enhanced by roughly 30 compared using the GF-3 information alone. This is primarily as a result of the consideration with the biophysical and biochemical alterations that take place with all the change within the phenology of wetland kinds.Table four. Accuracy assessment final results obtained by ML, MD, and SVM procedures for GF-3, OHS, and synergetic data sets. Accuracy Metric General Accuracy Information GF-3 OHS GF-3OHS GF-3 OHS GF-3OHS ML 52.five 96.7 97.three 0.36 0.95 0.96 MD 55.7 87.six 89.0 0.41 0.82 0.84 SVM 80.7 95.9 97.two 0.70 0.94 0.Kappa CoefficientRemote Sens. 2021, 13,15 ofFigure 9. The general accuracy (OA) and Kappa coefficient obtained by ML, MD, and SVM strategies for GF-3, OHS, and synergetic information sets. Table 5. PA for various wetland forms utilizing unique input feature sets and supervised classification approaches. Wetland GF-3 ML GF-3 MD GF-3 SVM OHS ML OHS MD OHS SVM GF-3OHS ML GF-3OHS MD GF-3OHS SVM Saltwater 43.6 61.74 93.97 99.05 87.three 98.9 99.03 93.31 99.05 Farmland 64.26 61.41 74.57 99.57 98.09 99.47 98.94 96.23 98.five River 1.six 61.43 0.62 99.64 93.4 97.08 98.99 96.05 95.69 Shrub 65.86 65.five 56.44 92.54 87.38 92.99 91.89 74.08 95 Grass 17.26 37.11 46.38 89.95 76.7 93.78 91.05 67.01 93.7 Suaeda salsa 35.48 29.03 0 83.06 71.77 80.65 68.55 74.19 81.45 Tidal Flat 82.5 42.88 78.81 91.99 85.28 88.03 94.96 82.98 93.Table six. UA for different wetland types using various input function sets and supervised classification solutions. Wetland GF-3 ML GF-3 MD GF-3 SVM OHS ML OHS MD OHS SVM GF-3OHS ML GF-3OHS MD GF-3OHS SVM Saltwater 91.46 92.47 88.2 99.98 100 99.82 99.94 99.97 99.73 Farmland 56.07 57.2 58.four 84.82 89.six 82.45 89.06 95.78 90.41 River 4.58 16.67 5.18 86.25 47.06 84.18 88.52 46.71 85.9 Shrub 48.93 48.47 55.14 91.26 79.48 98.66 89.76 63.78 one hundred Grass 58.37 40.98 60.49 96.03 93.32 96.46 94.88 70.19 92.15 Suaeda salsa 0.96 0.44 0 55.38 1.4 16.53 72.65 2.63 24.46 Tidal Flat 38.02 43.92 87.93 99.93 98.five 99.81 99.5 97.82 99.Remote Sens. 2021, 13,16 ofTable 7. F1-score for distinctive wetland forms applying unique input function sets and supervised classification approaches. Wetland GF-3 ML GF-3 MD GF-3 SVM OHS ML OHS MD OHS SVM GF-3OHS ML GF-3OHS MD GF-3OHS SVM Saltwater 59.05 74.04 90.99 99.51 93.22 99.36 99.48 96.53 99.39 Farmland 59.89 59.23 65.50 91.61 93.65 90.16 93.74 96.00 94.28 River two.37 26.22 1.11 92.46 62.59 90.17 93.46 62.85 90.53 Shrub 56.15 55.71 55.78 91.90 83.24 95.74 90.81 68.55 97.44 Grass 26.64 38.95 52.50 92.89 84.20 95.ten 92.93 68.56 92.92 Suaeda salsa 1.87 0.87 0.00 66.45 2.75 27.44 70.54 five.08 37.62 Tidal Flat 52.05 43.39 83.12 95.80 91.41 93.55 97.18 89.79 96.Figure ten. The PA (a), UA (b), and F1-score (c) obtained by ML, MD, and SVM methods for GF-3, OHS, and synergetic information sets.Figure 11. The con.