## 4 Results and Discussion
### Discussion  
As shown in Figure 14 and Table 5, except for the Multilayer Perceptron (MLP) and GoogLeNet models, which had a poor recognition accuracy for Feature 4, all the other models achieved high recognition accuracies for the remaining features, with accuracies above 90%. ResNet34 was closer to the SL-CNN model regarding its single-feature recognition accuracy, but the other evaluation indices showed significant differences, in which the kappa coefficient, OA, and AA values were reduced by 0.662, 0.409%, and 1.633%, respectively, compared to the SL-CNN model. The ResNet34 model's training time, the memory occupied by the generated model, and the number of total parameters of the model building run accounted for 18.21%, 6.60%, and 5.81% of the ResNet34 model, respectively. GoogLeNet had a high classification accuracy for Artemisia frigida and Bare Soil, but compared to the SL-CNN model, the classification accuracy of Feature 3 was significantly lower by 3.58%. Its generated model and the number of total parameters of the model building run, the memory occupied by the total number of parameters for the build run were 93.4 MB and 28.76 MB, respectively. This represented increases of 82.55% and 83.56% compared to the SL-CNN model. DenseNet121 had a single feature classification accuracy approximately similar to that of ResNet34 and possessed approximately the same memory occupation as GoogLeNet. However, the model training time was 72.487% that of ResNet34. The MLP had the lowest classification accuracy among all of the models, with an AA value of 72.487%. The detailed analysis results showed that the MLP is a fully connected network model with a simple model structure and limited feature extraction ability, resulting in the lowest classification accuracy for the fine features in desert grasslands. However, the model training time was shorter. In contrast, GoogLeNet used multiple parallel convolutional  
Figure 14: Comparison of confusion matrices for different models (**a**) ResNet34; (**b**) GoogLeNet; (**c**) DenseNet121; (**d**) MLP; (**e**) 2D-CNN; and (**f**) SL-CNN. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **ResNet34** & **GoogLeNet** & **DenseNet121** & **MLP** & **2D-CNN** & **SL-CNN** \\ \hline Kappa \(\times\) 100 & 98.073 & 96.716 & 98.032 & 93.271 & 98.690 & 98.735 \\ OA (\%) & 98.807 & 97.967 & 98.791 & 95.849 & 99.188 & 99.216 \\ AA (\%) & 96.809 & 94.187 & 98.047 & 72.487 & 98.403 & 98.442 \\ Train time (s) & 2015 & 600 & 1491 & 25 & 557 & 367 \\ Memory (MB) & 247.0 & 93.4 & 92.2 & 21.7 & 55.8 & 16.3 \\ Model params(MB) & 81.38 & 28.76 & 27.02 & 7.21 & 17.91 & 4.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different model classifications. branches to capture grassland features at different scales and levels, which enhanced the model structure and network depth and improved the network expression ability. However, the model complexity was not high, and the features were not fully extracted, so the classification accuracy was limited. ResNet34 and DenseNet121 used a residual structure and dense connection structure, respectively. This increased the complexity and depth of the network, addressed the problems of gradient disappearance and information loss to the greatest extent, and improved the performance of fine-grained classification. However, they also introduced more operational parameters, which increased the model construction time and memory requirements. The SL-CNN model was different from the four conventional models mentioned above, especially ResNet34 and DenseNet121. It constructed the CBAM-F feature refinement module based on the improved depth-separable convolution by transforming the Shared MLP module in CBAM attention to 2D-CNN convolution. Additionally, SL-CNN also made full use of the residual structure to construct a residual block convolution feature enhancement module.