# Analysis of Airglow Image Classification Based on Feature Map Visualization
## 3 Experiment  
### Data Sets  
The airglow images for training were captured by the ASAI at Xinglong station, from 1 January 2012 to 31 December 2012. A single ASAI system includes a fish-eye lens, a near-infrared bandpass filter sensitive to OH emission (715-930 nm), a CCD detector, and an optical imaging system. The fish-eye lens has a 180\({}^{\circ}\) zenith angle of view. The effective observing radius for OH emission is about 400 km, limited by the increasing distortion at the edge. ASAI takes photos every 64 s, accumulating about 600 images per night. A total of 1688 representative images were manually selected and divided into five categories: clear night, overcast sky, light band, moon, and twilight (shown in Table 1). Among all the labeled images, 1400 images (approximately 83.0%) were used as the training set and the remaining 288 images (17.0%) were used as the validation set. Images with a representative shape and brightness distribution were selected to enhance the generalization of the classification model. In order to avoid overfitting caused by similar images, at most, one photo is selected out of ten consecutive photos. The number of images assigned to each category depended on the actual number of representative images manually found in each category. For CNNs, the inclusion of a greater number of diverse images in the training process can greatly enhance the robustness of the model's performance. Horizontal rotation of images is a simple yet effective method of data augmentation. The image matrixes generated from the existing training data by rotation have a modified arrangement of pixels but the same features for classification. To enlarge the training and validation sets, we included three rotated versions of images--anticlockwise rotation by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), respectively--into the training and validation set, quadrupling the number of images in both sets. In the first attempted training, whole images were input to train the image classifier model, but the actual classification accuracy was much lower than the theoretical prediction. The fitted parameters of the full connection layer in the model were visualized to illustrate the feature map (shown in Figure 2). In the feature map, there are several pixels of high weight (bright ones) at the edge of the round version field, suggesting that the model focuses on the artificial lights on the horizon instead of the stars when classifying. To cut out the artificial lights around the image margins and prevent confusion between stars and artificial lights (shown in Figure 3), a square containing 512 \(\times\) 512 pixels was cropped at the center of each original image. Since the 512 \(\times\) 512 square at the center includes the whole effective observation range, replacing the original image with the cropped region will not lead to information loss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Explanation** & **Images in the Training/** \\  & & **Validation Set** \\ \hline \multirow{3}{*}{clear night} & Stars can be seen clearly; there are no & \\  & apparent intense light sources other & 457/105 \\  & than stars. & \\ \hline \multirow{2}{*}{overcast sky} & No light can be seen; completely dark. & 405/82 \\ \hline \multirow{3}{*}{light band} & There are obvious/unignorable intense & \\  & light sources other than stars, such as the & 103/22 \\  & light band caused by intense moon light. & \\ \hline \multirow{3}{*}{moon} & Stars cannot be easily discerned due to & \\  & extensive areas of intense moon light; there & 361/72 \\  & are still darker areas in the image. & \\ \hline \multirow{3}{*}{twilight} & Stars cannot be recognized due to the & \\  & extremely intense light emitted by sun; & 67/14 \\ \cline{1-1}  & completely white. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categories of images captured by all-sky airglow imager (ASAI). Figure 2: (**a**) Original size (1024 \(\times\) 1024 pixels) airglow image; (**b**) three-dimensional view of feature map.