## 3 Experiments and Result Analysis
### Results and Analysis
#### 3.3.2 Comparison of the Classifier with Different Classification Methods  
In this section, we compare the classification performance of feeding the 9-dimensional features, which are extracted from NC-SAE, into SVM, CNN and MSCDN classifiers. The classification results and error maps for above classifiers are shown in Figure 8. It can be readily seen that the proposed MSCDN classifier behaves the best performance. In order to provide the insight into above result, we further show the OA performances of the different classifiers, along with the recall rates for each crop in Table 5. One sees that the OA performance of MSCDN is 24% and 5% higher than that of SVM and CNN. Observing the recall rate for each crop in Table 5, we see that the poorer OA for SVM and CNN is mainly due to the low recall rates of several individual crops (namely Duw: Durum Wheat, Mip: Mixed Pasture, Mih: Mixed Hay, and Chf: Chemical fallow). By further analyzing the categories of these crops in Table 3, we find that the above mentioned crops are easily confused with others because they have the same growth cycle or similar external morphologies with others. For example, Duw (Durum Wheat) is similar to Spw (Spring Wheat) in terms of external morphology, and Mip (Mixed Pasture) is more easily confused with Gra (Grass) and Mih (Mixed Hay). We conjecture that the poorer OA for SVM and CNN arise from the poorer distinguishable features that extracted by their network architectures.  
Figure 7: Classification results and error maps: (**A–D**) are the classification results maps of different dimension reduction methods which are (**A**) LLE + CNN, (**B**) PCA + CNN, (**C**) S-SAE + CNN, (**D**) NC- SAE + CNN. (**a–d**) are the error maps of (**A–D**).  
From the above analysis, we see that the accurate classification for these easily confused crops is the key point of enhancing the overall accuracy. For deeply understanding the improvement of our MSCDN classifier, the confusion matrix of crops Duw, Mip, Mih and Chf for CNN and MSCDN are shown in Table 6. One sees that compared to CNN, MSCDN greatly improves the recall rates of these easily confused crops, whose averaged recall rate increased more than 31%. This is not surprising because MSCDN is a multi-scale neural network, the architecture of which enables to extract the features in different scales by using multiple kernels in convolution layers, and hence MSCDN is able to capture the tiny distinctions between the feature maps. Moreover, it should be pointed out that the above easily confused crops have very small samples in our crop data (only 7.3% of whole samples). Therefore, the improvement of OA performance for MSCDN will be foreseen.  
Figure 8: Classification results for different classifiers (**A**) SVM, (**B**) CNN, (**C**) MSCDN; (**a–c**) are the error maps of (**A–C**).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{10}{c}{**Recall Rates for 14 Types of Crops and OA (\%)**} \\ \cline{2-13}  & **Len** & **Duw** & **Spw** & **Fip** & **Oat** & **Can** & **Gra** & **Mip** & **Mih** & **Bar** & **Suf** & **Fla** & **Cas** & **Chf** & **OA** \\ \hline SVM & 91.4 & **1.8** & 98.3 & 93.9 & 2.3 & 95.3 & 69.5 & **8.5** & **42.1** & 3.0 & 45.4 & 40.8 & 5.8 & **0** & **75.22** \\ CNN & 97.8 & **69.2** & 96.5 & 99.7 & 80.5 & 99.8 & 89.4 & **69.0** & **76.3** & 84.6 & 92.2 & 93.3 & 90.2 & **42.9** & **94.23** \\ MSCDN & 99.6 & **98.4** & 99.6 & 99.8 & 97.9 & 99.9 & 98.6 & **97.3** & **96.4** & 98.1 & 98.9 & 98.5 & 99.4 & **89.9** & **99.33** \\ \hline \hline \end{tabular} Note: The numbers in columns using bold demonstrate the improvements of the recall rates and OA.  
\end{table}
Table 5: The recall rates and OA of crop classification of different classifier after NC-SAE dimensional reduction.