# Analysis of Airglow Image Classification Based on Feature Map Visualization
###### Abstract  
airglow images  
of CNNs [14]. In 1942, [PERSON] invented the backpropagation (BP) algorithm [15], which gained recognition through the works of [PERSON] et al. [16]. In 1990, [PERSON] et al. [17] applied the BP algorithms to handwritten digit recognition. In 1998, [PERSON] et al. [18] proposed the \"LeNet-5\" CNN architecture by summarizing the end-to-end training principle of modular systems, greatly improving the performance of standard handwritten digit recognition. However, the shallow machine learning models encountered problems such as local optimum, overfitting, and vanishing gradient as the number of networks increased [19; 20; 21]. In 2006, [PERSON] et al. [22; 23] demonstrated the excellent feature learning capacity of the artificial neural network with multiple hidden layers. In 2010, it was posited by [PERSON] et al. [24] that the vanishing-gradient problem could be mitigated by normalized initialization. Since then, CNNs have proved to be effective in various fields of visual recognition [25; 26; 27; 28].  
The visualization algorithm of neural networks opened the 'black box' of deep neural networks to analyze and modify trained models [29; 30; 31]. [PERSON] et al. [22] were the first to realize that the network in a region of the parameter can be initialized by unsupervised models. Then [PERSON] et al. [32] used gradient ascent in image space to optimize and find the input image that maximizes the neuron activity of interest, thus visualizing the hidden feature layer of unsupervised deep architectures. To reveal the convolution layer, [PERSON] et al. [33] proposed to project the feature activations back to the input pixel space through deconvolutional neural network architecture [34]. Based the calculation of the gradient of the class score, [PERSON] et al. [35] initially generated an image maximizing the class score. Thus, the notation of the class of CNN is visualized.  
In our previous research [36], we trained a classification model that divides airglow images into eight categories to filter out the pictures of unclear nights. Due to the lack of supervision of the training process, we extracted the model after 30 epochs by experience. In this paper, we trained an image classifier with higher speed and accuracy based on a CNN to select the airglow images captured by ASAI under ideal weather conditions, aiming to improve the atomization of the Meridian Project [37]. The classification model discussed in this paper is the optimized edition of the old ones with improved speed and accuracy. According to the feedback in the application, the number of categories was reduced from eight to five for less computation. With the visualized feature maps of the model generated after every epoch, we tracked the evolution of the classification model and chose the best one for further application. This paper is arranged as follows: theories of CNN and visualization of feature maps were introduced in Section 2. Section 3 presents the data set and training process. Based on visualized feature maps, the training process and overfitting are discussed in Section 4. The conclusions are given in Section 5.