###### Abstract  
Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches. image quality; remote sensing; scene classification; deep learning; explanation artificial intelligence +
Footnote †: journal: Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan  
Footnote †: journal: Medical Informatics, Chung Medical University Hospital, Taichung 402, Taiwan  
detection algorithm and Gaussian smoothing function to identify the relevant region in an image, [PERSON] et al. [4] suggested region-based processing to discard background information and obtain a faster converge for deep learning model training. The image quality assessment method is used to quantify the level of image accuracy, which can be classified as subjective and objective methods. The subjective method is based on the user's perception of the image, whereas the objective method is based on the mathematical calculation model for image quality prediction. [PERSON] [5] extracted visual features by attaching global average pooling layers to multiple inception modules of an ImageNet database pre-trained convolutional neural network for no-reference image quality assessment. Within the model of the deep neural network of supervised learning, if we can figure out where the distinctive feature texture of the region in the image, it will be a help for predicting and would provide significant assistance in the model development. Thus, using explainable artificial intelligence (XAI) technology is very useful for image analysis of model training, which includes understanding the reasonable and interpretable relationship between model prediction and image characteristics [6; 7]. When we can explain how the model performs task classification analysis, we can use it to assess whether the model decisions are reasonable. We can modify the model to ensure its reliability, thereby utilizing an interpretable feature correction method to further improve the accuracy of model training. We previously proposed a deep CNN architecture (i.e., RSSCNet) [8] for remote sensing scene classification tasks, and then used the Local Interpretable Model-Agnostic Explanation (LIME) [9] to analyze misclassified cases. Finally, we confirmed that the image quality will affect the classification performance of deep learning models. The primary difference between this and the previous study [8] is that this study proposes an automated image quality enhancement strategy as a preprocessing procedure that can improve the RSSCNet model's generalization ability. However, the previous study [8] only performed a manual image correction process for the four misclassified images by the RSSCNet model. This manual image correction process [8] is postprocessing, labor-intensive, and time-consuming procedure.