# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 1 Introduction  
With the gradual advancement of technology today, smart mobile devices and aerial cameras are beginning to appear on the market. As the performance of the hardware improves, aerial photography technology is constantly improving along with it, and rapid breakthroughs in imaging technology have made it possible to acquire imagery quickly. There are more types of imagery than ever before and the imagery is also clearer than was possible previously. Remote sensing images can be used in many technical aspects of scene classification, such as land-cover detection, urban planning, disaster relief, traffic control, etc. Therefore, how to classify large amounts of remote sensing image data covering land areas is an important research topic [1; 2; 3; 4; 5; 6; 7; 8]. In the past, when using image data for classification research, the primary focus was on how to effectively perform the task of feature extraction [9; 10]. The deep-learning methods used today make use of different convolutional neural network models to automatically perform feature recognition, extract the required image details, and then train the classification model to recognize the scene. The popularization of graphics cards in recent years has enabled the amount of time spent on deep learning in neural network model training to be reduced. It has also enabled the rapiddevelopment of deep-learning research and studies related to the classification of high-resolution remote sensing images [11; 12; 13]. Related parameters used in deep-learning models include the training methods, network architecture, optimizer design, hardware operation, etc. Adjusting the model training hyper-parameters is a very important aspect of the model design. [PERSON] [14] proposed a new learning-rate method; instead of a fixed value, a cyclical learning policy was set for the model being trained. The results showed that this could effectively reduce the number of training iterations and improve the accuracy of the classification. [PERSON] and [PERSON] [15] then proposed a new super-convergence one-cycle cyclical learning policy and suggested the usage of a large learning rate for model training, which, according to them, can improve the model's generalization capability. More recently, [PERSON] and [PERSON] [16] explored the impact of different learning rates on deep learning and found that the low and high values of cyclical learning rates [14; 15] concur with their two regimes. Training is the process of learning and adjusting the parameters of a model. During training, iteration methods such as the gradient descent learning algorithm are commonly used. Early stopping [17] is a method often used during training to prevent overfitting. This method calculates the accuracy of the test dataset during model training. When the accuracy of the test data no longer improves, the training stops and is terminated early. This not only helps to prevent overfitting of the training model but also improves the model's generalization ability. This study aimed to produce an improved model with enhanced detection ability and a small number of training iterations. A two-stage circular learning method for training was, therefore, proposed. First of all, the image features were obtained by transfer learning; the classification framework designed in this paper was then used for training. The two-stage circular learning rate was used to reduce the number of iterations and, finally, the best model weights were obtained using the early stopping method. The main contributions of this paper are as follows:  
* In order to reduce model overfitting and to obtain models with a high generalization capability, we recommend a new CNN (convolutional neutral networks) architecture, i.e., RSSCNet (remote sensing scene classification network), integrated with the simultaneous use of a two-stage cyclical learning-rate training policy and the no-freezing transfer learning technology that requires only a small number of iterations. In this way, an excellent level of accuracy can be obtained.