## 4 Experimental Setup
### Experimental Details
```
Input: Datasets \(\{\mathcal{D}_{H},\mathcal{D}_{X},\mathcal{D}_{A},\mathcal{D}_{P}\}\), Model \(AMSDM\), Epochs \(E\), Learning rate \(\eta\), Batch size \(B\) Output: Trained model \(AMSDM\) with optimal performance metrics Initialize model parameters \(\Theta\) for \(AMSDM\) Set initial learning rate \(\eta_{0}=0.001\) and decay factor \(\gamma=0.1\) Set early stopping patience \(p=5\), maximum epochs \(E=50\) Initialize performance metrics: \(Acc\), \(P\), \(R\), \(mAP\) for each dataset \(\mathcal{D}\in\{\mathcal{D}_{H},\mathcal{D}_{X},\mathcal{D}_{A},\mathcal{D}_{P}\}\)do  Split \(\mathcal{D}\) into training and validation sets (\(80\%:20\%\))  Normalize dataset using SAR-specific mean \(\mu\) and standard deviation \(\sigma\) for\(epoch=1\) to \(E\)do  Initialize epoch loss \(\mathcal{L}_{\text{total}}=0\) for each mini-batch \((X,y)\) of size \(B\) in training datado  Apply augmentations (e.g., rotations, flips, and Gaussian noise) to \(X\)  Compute predictions \(\hat{y}=AMSDM(X;\Theta)\)  Calculate and accumulate loss: \(\mathcal{L}_{\text{total}}\leftarrow\mathcal{L}_{\text{total}}+\mathcal{L}( \hat{y},y)\)  Backpropagate and update parameters: \(\Theta\leftarrow\Theta-\eta\
abla_{\Theta}\mathcal{L}\) using SGD with momentum \(m=0.9\)  end for ifepoch % 10 == 0 then  Adjust learning rate: \(\eta\leftarrow\eta\times\gamma\)  end if  Compute validation metrics: \(Acc_{\text{val}}\), \(P_{\text{val}}\), \(R_{\text{val}}\), \(mAP_{\text{val}}\)  Log validation performance metrics (\(Acc_{\text{val}}\), \(P_{\text{val}}\), \(R_{\text{val}}\), \(mAP_{\text{val}}\)) ifAcc\({}_{\text{val}}\) improvesthen  Save best parameters: \(\Theta_{\text{best}}\leftarrow\Theta\)  Reset patience counter  end if ifno improvement in \(Acc_{\text{val}}\) for \(p\) epochsthen Break (early stopping)  end if  Print epoch summary: \(\mathcal{L}_{\text{total}}\), \(Acc_{\text{val}}\), \(P_{\text{val}}\), \(R_{\text{val}}\), \(mAP_{\text{val}}\)  end if  end for for each test dataset \(\mathcal{D}_{t}\in\{\mathcal{D}_{H},\mathcal{D}_{X},\mathcal{D}_{A},\mathcal{D}_ {P}\}\)do  Compute test metrics (\(Acc_{t},P_{t},R_{t},mAP_{t}\)) using \(\Theta_{\text{best}}\)  Log test results for \(\mathcal{D}_{t}\): (\(Acc_{t},P_{t},R_{t},mAP_{t}\))  end for Output: Optimized model parameters \(\Theta_{\text{best}}\) and comprehensive performance metrics
```  
**Algorithm 2**Adaptive Multi-Stage Deep Model (AMSDM) Training and Evaluation Algorithm  
To evaluate the proposed model comprehensively, a combination of computational and predictive performance metrics were used. Training time, denoted as \(T_{\text{train}}\), measures the total time in seconds required to train the model, reflecting computational efficiency during the learning phase. Inference time, \(T_{\text{infer}}\), represents the average time in milliseconds taken to process a single sample, with lower values indicating suitability for real-time applications. The number of trainable parameters, \(P\), measured in millions, evaluates the model's complexity. Fewer parameters generally result in lower memory usage but may affect model capacity. Computational complexity was assessed using floating point operations (FLOPs), denoted as \(F\) and measured in billions. Lower FLOP values suggest better computational efficiency. Performance was further assessed using accuracy, recall, and F1-score. Accuracy, calculated as  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}},\]  
measures the overall correctness of predictions. Recall  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}},\]  
evaluates the model's ability to identify relevant instances. F1-score  
\[\text{F1}=2\cdot\frac{\text{Precision}\cdot\text{R}}{\text{Precision}+\text{R}},\]  
provides a balanced metric that accounts for both precision and recall. By balancing computational and performance metrics, this evaluation approach ensures a comprehensive assessment of the model's efficiency and predictive capabilities, highlighting its practicality for real-world deployment.