# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 5 Discussion  
### Effect of Features on Accuracy  
At present, remote sensing image classification mainly relies on three feature types: spectral features mainly express information for individual pixels, textural features mainly express the spatial correlation between adjacent pixels, and semantic features mainly express the relationship between pixels in a specific region in a more abstract way.  
CNNs can simultaneously extract these three feature types by reasonably organizing the convolution kernel. For example, the features extracted by using a \(1\times 1\) convolution kernel are equivalent to spectral features, those obtained by shallow convolutional layers can be regarded as textural features, and those obtained by convolutional layers of different depths can be regarded as semantic. Therefore, advanced semantic features can be considered as including all three feature types. However, in CNNs, advanced semantic features are obtained by deepening the network structure and expanding the receptive field. The spatial resolution of the image, the number of pixels covered by the object area, and other factors will affect the feature extraction results.  
In a high-resolution and detailed camera image, an object tends to occupy a larger area, and it is advantageous to use advanced features. When SegNet only uses high-level semantic features to process an image with a high level of detail, a certain number of misclassified pixels occur at the edge, but few occur inside the object.  
In the experiments of this study, the results of SegNet contained more misclassified pixels both at the edge and the inner area. Comparing objects of different sizes showed that smaller objects had more other types of objects adjacent to them and more pixels filled in. This is because the receptive field of high-level semantic features is generally large, and when the type distribution of objects in the receptive field is messy, the extracted feature values will deviate greatly from those of other pixels of the same kind, thus, affecting the accuracy of the segmentation results.  
Unlike SegNet, the other models tested here combined low-level semantic features with high-level semantic features, with more accurate results. When segmenting remote sensing images with high spatial resolution, such as GF-6, the different levels of semantic features should be fused and used for classification, which is more reasonable than using advanced semantic features alone.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **SegNet** & **U-Net** & **RefineNet** & **WFCNN-1** & **WFCNN-2** & **WFCNN** \\ \hline Accuracy & 75.58\% & 81.75\% & 82.67\% & 85.48\% & 91.13\% & 94.13\% \\ Precision & 75.05\% & 69.62\% & 76.07\% & 81.65\% & 89.90\% & 91.93\% \\ Recall & 72.20\% & 74.16\% & 82.02\% & 81.72\% & 90.71\% & 94.14\% \\ F1-Score & 0.7101 & 0.7797 & 0.7904 & 0.8231 & 0.8906 & 0.9271 \\ IoU & 0.7360 & 0.7181 & 0.7893 & 0.8169 & 0.9031 & 0.9302 \\ Kappa coefficient & 0.5826 & 0.5652 & 0.6514 & 0.6905 & 0.8232 & 0.8693 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of model indicators on the GF-6 images dataset comparison.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Indicator** & **SegNet** & **U-Net** & **RefineNet** & **WFCNN-1** & **WFCNN-2** & **WFCNN** \\ \hline Accuracy & 78.50\% & 86.40\% & 92.80\% & 94.20\% & 95.60\% & 96.90\% \\ Precision & 76.94\% & 86.19\% & 92.83\% & 94.21\% & 95.22\% & 96.74\% \\ Recall & 74.43\% & 83.41\% & 91.23\% & 92.97\% & 95.10\% & 96.43\% \\ F1-Score & 0.6117 & 0.7362 & 0.8522 & 0.8794 & 0.9077 & 0.9340 \\ IoU & 0.7566 & 0.8478 & 0.9202 & 0.9358 & 0.9516 & 0.9658 \\ Kappa coefficient & 0.6122 & 0.7355 & 0.8520 & 0.8793 & 0.9080 & 0.9341 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of model indicators on the aerial image labeling dataset.