## 2 Related Work  
Currently, image-based damage assessment research is mainly categorized into change detection methods and machine learning-type methods. In the first type of method, the images of the target area before and after the strike are detected by the change detection method, and the damage assessment is carried out by utilizing the change feature quantity or its variants. First, feature quantities such as change pixel value, change area mean value, and change area can be directly utilized to calculate the damage assessment results. For example, [PERSON] [9] used the percentage of effective change to effective area to judge the degree of target destruction. Secondly, the geometric and texture features, such as \"quadratic distance\", can be calculated after the change detection and used to construct the damage assessment level calculation model. For example, [PERSON] et al. [10] designed hierarchical assessment criteria, the use of the overall feature vector \"similarity\", geometric and texture features, and \"secondary distance\" characteristics for the damage assessment of airports. [PERSON] [11] designed different damage characteristics for different strike targets, the geometric features of the aircraft target, and the damage characteristics of the aircraft target. The following destruction characteristics are targeted to determine the degree of destruction of various types of targets: damage characteristics, the rate of change of geometric features of aircraft targets, texture features, etc. ; the number of craters per unit length of the bridge target, the area of damage per unit length, the number of truncated areas, etc. ; and after the runway target is damaged, whether there is a rectangular area on the runway target to meet the needs of aircraft takeoff. [PERSON] et al. [12], for the design of ship targets, extracted geometric features and texture features for the assessment of damage to airports. [PERSON] et al. [12] extracted geometric features and texture features for ship target design to carry out grading assessment. Again, we can also analyze the functional characteristics of the target based on the target characteristics and design and the conversion method between the target image change features and the target functional features so as to carry out functional damage assessment of the target. For example, [PERSON] et al. [13] designed the longest takeoff length index of the airport runway based on the functionality of the airport. [PERSON] et al. [14] judged the degree of damage by the number of effective takeoff and landing windows, the residual retention ratio of the number of takeoff and landing windows, and the length of the takeoff and landing window retention ratio, and [PERSON] [15] designed the narrowest through-path based on the bridge's functionality for through traffic in the literature. Finally, the feature quantities calculated by each method can be fused to design a feature fusion model to enhance the robustness of the damage assessment model. For example, [PERSON] et al. [16] used the damage length ratio of the runway area, the percentage of damage in the contact area, and the damage area ratio of the airfield area to judge the degree of damage. [PERSON] et al. [17] introduced the fuzzy consistency matrix-based fuzzy analytic hierarchy process (FAHP) method to determine the weights of physical damage, functional damage, and recovery ability indicators to derive the target's damage level, and [PERSON] et al. [18] classified the target into multiple regions of interest according to the importance of its components and then analyzed the target's damage level according to the importance of the target's functionality. [PERSON] et al. [18] divided the target into several regions of interest and then independently calculated the damage indicators for each region and weighted them together to realize the damage assessment.