# Analysis of Airglow Image Classification Based on Feature Map Visualization
## 2 Structure of the Convolutional Neuron Network
After a feature is detected, the precise location of the feature is not as important as its relationship with other features in terms of relative position; this is the reason why this mechanism is effective. Moreover, by applying the max-pooling, the representation becomes invariant to small local translations. The fourth layer is the dropout layer. The dropout layer drops the outputs of neurons in the network at random to prevent overfitting. There are 64 \(\times\) 64 \(\times\) 16 neurons in the  
Figure 1: The network structure of the CNN classification model. The number below indicates the size of matrix of each layer. dropout layer, all of which are connected, respectively, with the neurons in the previous max-pooling layer. Every neuron in the dropout layer is assigned a random number between 0 and 1, which is regenerated at every iteration during training. The neuron will output the data received from the previous layer if its random number is greater than \(p\) (set to be 0.5); otherwise, the neuron will temporarily output 0. In 2012, [PERSON] et al. [39] first proposed the dropout layer as a solution to the problem of overfitting in large feedforward neural networks when trained on small training sets. By randomly omitting half of the feature detectors in each training case, overfitting can be greatly reduced, and dropout also prevents complex co-adaptations. Dropout achieves lower errors and cross-entropy. It significantly controls overfitting and makes the method robust to network architecture choices. In the same year, [PERSON] et al. [25] used the dropout algorithm, setting the output of each hidden neuron to zero with a probability of 0.5. In this way, the neurons that are shut off do not contribute to the forward propagation and do not participate in the backward propagation. As a result, every time an input is presented, the neural network samples different architectures, but all these architectures share the same weights. This technique reduces complex co-adaptations of neurons, as one neuron cannot rely on the existence of specific other neurons. The next three layers are the second convolutional layer, second max-pooling layer and second dropout layer, respectively. These three layers operate the same as layers from two to four, except the second convolutional layer is scanned by 128 kernels of size \(5\times 5\times 16\). The eighth layer is a flatten layer, in which the input, a 32 \(\times\) 32 \(\times\) 128 matrix, is flattened into a one-dimensional matrix with 131,072 elements. The output from this layer is then passed through the ninth layer, which is another dropout layer with the same parameter setting as the seventh layer. After being filtered by randomly dropping out, the processed data (a 1 \(\times\) 131,072 array) are input to the full connection layer (the tenth layer). There are five neurons in the full connection layer, each corresponding to a specific category: clear night, overcast sky, light band, moon, and twilight. Every neuron in the full connection layer is connected to all neurons in the previous dropout layer. The value \(C_{u}\) of the \(u\)-th neuron in the connection layer is derived by weighted summation of all the elements in the input one-dimensional array:  
\[C_{u}=\sum_{v=1}^{k}w_{u,v}\cdot n_{v}u=1,2,\ldots 5;k=131,072, \tag{3}\]  
the \(w_{u,v}\) is the weight associating the \(v\)-th input element and the \(u\)-th neuron in the full connection layer; \(n_{v}\) is the \(v\)-th element in the input array. To improve the expression of classification result of input image m, the output of the full connection layer is normalized by SoftMax function, resulting in possibilities \(p(m)_{u}\) to determine the possible category \(u\):  
\[p(m)_{u}=\frac{e^{C_{u}}}{\sum_{k=1}^{5}e^{C_{k}}}. \tag{4}\]  
The output possibilities of all categories of an input image \(m\) add up to 1. The image is classified into the category with the greatest possibility.