# ISAR Image Quality Assessment Based on Visual Attention Model
## 3 Experimental Setup and Analysis of Results  
We collect the dataset by searching for ISAR images on the Internet and make simulation ISAR images. We then acquire 564 ISAR images containing satellite targets, aircraft targets, vehicle targets, ship targets, etc. ISAR images of satellite targets, ISAR images of aircraft targets, ISAR images of vehicle targets, and ISAR images of ship targets each account for about 1/5 of the total. The remaining 1/5 images are cluttered and blurred images. The experiments described in this paper were conducted using an NVIDIA GeForce RTX 2080 graphics card (NVIDIA, Santa Clara, CA, USA) with PyTorch v1.8. Input images were cropped to a size of 224 \(\times\) 224 pixels, with a vertical flip probability set at 0.5. Each input consisted of a batch size of 8 images. For feature extraction, we utilized a pre-trained ViT model as the backbone. Due to the confidentiality of ISAR images, we created an ISAR image database using simulation software along with collected images. The ISAR images were divided into a training set and a test set in an 80:20 ratio, with labels assigned based on the average scores given by professionals. We recognize that the dataset, comprising 564 ISAR images, may not be sufficiently large or diverse to fully capture the variability and complexity of real-world scenarios. To address these concerns, we have implemented several strategies:  
Data Augmentation: We have applied data augmentation techniques to artificially expand the dataset, introducing variations that can help the model learn more robust features. Cross-Validation: We have employed cross-validation to ensure that the model's performance is consistent across different subsets of the data, thereby reducing the risk of overfitting to a particular subset. The evaluation criteria are SRCC and PLCC, with SRCC being known as the Spearman rank-order correlation coefficient (SRCC), which measures the similarity of two datasets. The value range is 0-1, and when the output value is 1, it indicates that the two datasets are identical. \[SRCC=1-\frac{6\sum_{i=1}^{N}{d_{i}}^{2}}{N(N^{2}-1)} \tag{8}\]  
\(N\) denotes the number of samples, and \(d_{i}\) denotes the difference between the subjective quality score ranking of the i-th image and the objective quality score ranking. The Pearson linear correlation coefficient (PLCC) describes the correlation between the objective evaluation scores of the algorithm and the subjective scores of the human eye. \(s_{i}\)_and_\(\widehat{s}_{i}\) denote the predicted quality score and objective quality score of the ith image,respectively. \(\mu_{s_{i}}\) and \(\mu_{\tilde{y}_{i}}\) denote the mean predicted quality score and the mean objective quality score, respectively. \[PLCC=\frac{\sum_{i=1}^{N}(s_{i}-\mu_{s_{i}})\big{(}\tilde{s}_{i}-\mu_{\tilde{s}_{ i}}\big{)}}{\sqrt{\sum_{i=1}^{N}(s_{i}-\mu_{s_{i}})^{2}}\sqrt{\sum_{i=1}^{N}( \widehat{s}_{i}-\mu_{\tilde{s}_{i}})^{2}}} \tag{9}\]  
Figure 4 shows the loss variation curve, SRCC variation curve, and PLCC variation curve, respectively. The number of epochs is on the abscissa, while the value on the ordinate represents the measurement value. It can be seen that the loss in the training process decreases with the epoch number up. The PLCC grows stably while the SRCC improves to a maximum of 0.9103. In the first 50 epochs, the fitting ability of the model is increasing and the performance of our model on the dataset is improving. We show some typical ISAR images including satellite targets and ship targets in Figure 5. The first image is of average quality, the middle two images are of good quality, and the last image is of medium to good quality, effectively representing the overall distribution of the dataset. The ground truth score and prediction score are displayed below the images. Notably, there is only a small gap between the ground truth score and the predicted score, indicating that our model achieves optimal performance.