## 4 Discussion  
After correcting the image dataset by different correction methods in this study, it can be observed from the training results that through noise reduction, sharpening, and partial image enhancement, the images are processed through a consistent pixel enhancement method to achieve an automated method for optimal image quality. The automated quality enhancement strategy helps to improve the accuracy of prediction. When accurate and effective corrections are made, this process can be used to strengthen model training to achieve better classification accuracy. In addition to discussing the different image correction methods, this study also hopes to yield an understanding of how the image feature texture enhancement is performed. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Training Ratio**} \\ \cline{3-4}  & & **40\%** & **60\%** \\ \hline GoogNet [20] & 2015 & 93.12 \(\pm\) 0.82 & 94.71 \(\pm\) 1.33 \\ CaffNet [20] & 2016 & 95.11 \(\pm\) 1.20 & 96.24 \(\pm\) 0.56 \\ VGG-16 [20] & 2016 & 95.44 \(\pm\) 0.60 & 96.05 \(\pm\) 0.91 \\ TEX-Net-LF [24] & 2018 & 97.61 \(\pm\) 0.36 & 98.00 \(\pm\) 0.52 \\ Two-Stream Fusion [25] & 2019 & 98.23 \(\pm\) 0.56 & 98.92 \(\pm\) 0.52 \\ SE-MDPMNet [26] & 2019 & 98.46 \(\pm\) 0.21 & 98.97 \(\pm\) 0.24 \\ RSSCNet [8] & 2020 & 98.54 \(\pm\) 0.37 & 99.46 \(\pm\) 0.21 \\ EfficientNetB3-Attn-2 [23] & 2021 & 98.60 \(\pm\) 0.40 & 98.68 \(\pm\) 0.93 \\ RSSCNet w/improved image (our) & 2021 & 98.71 \(\pm\) 0.23 & 99.58 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the overall accuracy on WHU-RS19 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Training Accuracy**} & \multirow{2}{*}{**Test Accuracy**} & \multirow{2}{*}{**Test Correct Cases**} \\ \hline Original Image & 1.0 & 0.9768 & 589 \\ Denoising Sharpen with CLAHE & 1.0 & 0.9801 & 591 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effect of the proposed method on WHU-RS19 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Training Ratio**} \\ \cline{3-4}  & & **20\%** & **50\%** \\ \hline DBN [10] & 2015 & NA & 77.00 \\ GoogLeNet [20] & 2016 & 82.55 \(\pm\) 1.11 & 85.84 \(\pm\) 0.92 \\ CaffNet [20] & 2016 & 85.57 \(\pm\) 0.95 & 88.25 \(\pm\) 0.62 \\ VGG-16 [20] & 2016 & 83.98 \(\pm\) 0.87 & 87.18 \(\pm\) 0.94 \\ Deep Filter Banks [21] & 2016 & NA & 90.4 \(\pm\) 0.6 \\ GCFs+LOFs [22] & 2018 & 92.47 \(\pm\) 0.29 & 95.59 \(\pm\) 0.49 \\ RSSCNet [8] & 2020 & 93.51 \(\pm\) 0.51 & 97.41 \(\pm\) 0.27 \\ EfficientNetB3-Attn-2 [23] & 2021 & 93.30 \(\pm\) 0.19 & 96.17 \(\pm\) 0.23 \\ RSSCNet w/improved image (our) & 2021 & 93.76 \(\pm\) 0.25 & 97.94 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the overall accuracy on the RSSCN7 dataset. Therefore, we use XRAI to visualize the feature regions of the hot zone map with the best prediction results, using this technology to perform an analysis of river/lake images in the RSSCN7 image dataset. As shown in Figure 13, the heatmap areas in the original image are concentrated on both sides, and the main feature block cut outs are distributed on the outer houses, which can explain the reason for the incorrect selection of the industry category. However, after fixing by noise sharpening and color correction, the hot zone moves to the central area, and the cropped main feature area also returns to the inner side of the lake, and then the correct classification category is obtained in the revised prediction. Figure 14 is also a river/lake image but was determined to be an incorrect field. After correction processing, the edge depth of the lakeside became strengthened and more evident. We can see the characteristic heatmap generated by XRAI, which is the primary focus area. The piece of land from the original outer edge was revised to the inner edge of the center of the lake.