## 2 Relate Work
### CNN and VGG 16  
A CNN is a widely used artificial neural network with good performance in various fields. It mainly consists of a convolutional layer, pooling layer, and fully connected layer. With the deepening of CNN research, researchers have proposed many improvement methods.  
The VGG is a model proposed by the Visual Geometry Group at the ImageNet Challenge, which can be used well for classification and localization tasks, as shown by the competition results and experimental performance [35]. Alexnet [36], which is the pioneering model, has the problems of a too-large convolutional kernel, too-large moving step size, and no padding. These problems are addressed by the VGG. It enhances the extraction of a small number of features and proves that increasing the depth of the network can affect the final performance. The VGG16, as the name suggests, has 16 network layers, including 13 convolutional layers and 3 fully connected layers, separated by max-pooling. All the activation units are processed using the ReLU function. The VGG16 uses 64, 128, 256, and 512 convolutional kernels of 3 \(\times\) 3 size in the first to the fifth parts of the convolutional layers, respectively, and a pooling layer of 2 \(\times\) 2 size is moved in steps of 2. The last convolutional layer uses 512 3 \(\times\) 3 convolutional kernels before feeding the features into the fully connected layer. After that, it performs the softmax prediction.  
The VGG choses to use multiple small-sized convolutional kernels to equal large-sized convolutional kernels in the convolutional kernel size setting. Multiple small-sized convolutional kernels produce fewer learning parameters than large convolutional kernels during feature extraction. As a result, the model is less computationally intensive and faster to train. The analysis shows that the VGG improves the performance of the model by increasing the depth of the network, with the network layer settings affecting the accuracy of the results. In 2021, [PERSON] et al. proposed a lightweight model of the VGG-16, which proved that the VGG is competitive in the field of remote sensing image processing [37]. In 2023, [PERSON] et al. discussed the migration learning capability of the VGG16 on HSIs through experimental validation. The experiments showed the performance accuracy of the VGG on HSIs [38].  
In terms of structure, the VGG16 mainly uses convolutional and pooling layers, which makes the structure relatively simple and easy to understand. Its 16-layer network helps it learn deeper features. One of the biggest features of the VGG16 is that the overall framework chooses smaller convolutional kernels, i.e., 3 \(\times\) 3-size convolutional kernels. This setup increases the nonlinear representation of the network and improves the accuracy of the model. The treatment of setting the same size convolution kernel for different convolutional layers enables the network to share parameters and improves the efficiency of the model. However, due to the deep layers in the VGG16, the VGG16 has a large memory requirement. Because of this, the VGG16 is prone to overfitting when there are insufficient data.  
To address the deficiencies exhibited by the VGG16, we chose to reduce the number of layers to lower the parameters and computation. To prevent a lack of features due to insufficient depth, we dropped the pooling layer to reduce the loss of valid information. We maintained the use of 3 \(\times\) 3 size convolutional kernels to preserve crucial features. Additionally, we retained the exponentially increasing characteristics in terms of channel settings to ensure feature retention.