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Image detection deep learning models are being used in various fields such as autonomous vehicles, medical, and agriculture. In the defense field, various efforts are being made to apply deep learning models to surveillance systems. Since the defense field deals with important issues such as combat, the reliability of the model is important. Therefore, this study intends to propose a new "strong supervised learning method" to ensure explainability of the defense image classification model. The proposed method is using the knowledge of military experts and the HITL (Human In The Loop) method in which humans and AI interact. This is the learning that provides the data of important parts of objects in addition to the general supervised learning which provides input data and labeling data. The learning process for the additionally provided data is carried out through a loss function of SSIM (Structural Similarity Index Map), which measures the similarity between data of important parts and a feature map of a convolutional neural network. In order to evaluate the proposed methodology, the model learned by general supervised learning and the model learned by the proposed method were compared. The comparison criteria were as follows: first, evaluation of similarity between the results of Grad CAM, a Visual Explain method, along with the data the important parts designated by experts; and second, the classification accuracy of objects. As a result of the experiment using the VGG16, ResNet50, InceptionV3, and Xception models, the similarity between the data of the Grad CAM and the region of interest was improved by an average of 15.52% based on the entire image area. In addition, the classification accuracy showed an average improvement of 4.72% based on Test Set. [ABSTRACT FROM AUTHOR]