# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 4 Experimental Results and Analysis
### Comparison with State-of-the-Arts
attention mechanism to focus on the effective bands of different land covers during feature extraction, achieves better anomaly detection results than the LRX algorithm. By effectively reducing noise in the reconstruction image, the proposed algorithm successfully detects nearly all anomalies and demonstrates a relatively high discrimination between anomalous and background pixels. The results of anomaly detection for the visualization of the AVIRIS airplane dataset are depicted in Figure 7. LRaSMD, AE-based and AUTO-AD algorithms can detect most of the anomalies. However, these detected anomalies often have low anomaly intensity values, and there are instances where the background is incorrectly identified as anomalous. The MCRD algorithm also detects anomalies with low intensity values, making it difficult to distinguish them from the background. Additionally, some anomaly targets are incorrectly classified as background pixels. Although the BJSR algorithm detects a large number of anomalies, it detects many background pixels as anomalies by mistakes and has a high false detection rate. LRX demonstrates better detection accuracy, but it still has a certain miss detection rate and incorrectly detects anomalies as background. The proposed hyperspectral anomaly detection algorithm based on the channel attention mechanism and LRX detects anomalies with higher intensity values than the LRX algorithm. This leads to a significant reduction in the probability of missed detections. Figure 6: Visualized anomaly detection results of different algorithms on the HYDICE urban dataset: (**a**) LRX; (**b**) BJSR; (**c**) LRaSMD; (**d**) MCRD; (**e**) AE-based; (**f**) AUTO-AD; (**g**) ours; (**h**) ground truth. Figure 7: Visualized anomaly detection results of different algorithms on AVIRIS airplane data set: (**a**) LRX; (**b**) BJSR; (**c**) LRaSMD; (**d**) MCRD; (**e**) AE-based; (**f**) AUTO-AD; (**g**) ours; (**h**) ground truth. The anomaly detection results visualized for the Salinas dataset are presented in Figure 8. LRX misses most of the anomalous targets, while BJSR incorrectly identifies many background pixels as anomalous targets. LRaSMD, MCRD, AE-based, and AUTO-AD algorithms outperform LRX and BJSR in the detection accuracy. However, there are still cases of misdetection, where some background pixels are incorrectly classified as anomalous targets. The proposed method in this paper exhibits a few missed detections for some anomalous targets, but performs well in distinguishing them from the background for the majority of anomalous targets. Therefore, the proposed algorithm in this paper achieves the best detection result. While the ROC curve provides qualitative insight into the performance of anomaly detection algorithms, it may not always clearly indicate which algorithm is superior. Therefore, quantitative analysis is necessary to facilitate better comparisons. The AUC value, which represents the area under the ROC curve, serves as an important evaluation metric. A larger AUC indicates better detection performance. The AUC values of the proposed algorithm and the six compared algorithms on the three hyperspectral datasets are presented in Table 2.  
From Table 2, it can be seen that the AUC values of the proposed algorithm in this paper are higher than those of the LRX, BJSR, LRaSMD, MCRD, AE-based, and AUTO-AD algorithms, which are 0.9871, 0.9916, and 0.9642 on the HYDICE urban dataset, AVIRIS airplane dataset, and Salinas Valley dataset, respectively. Therefore, the proposed algorithm in this paper achieves the best anomaly detection performance on all three datasets. The AUC values of the AUTO-AD method are 0.9646, 0.9764, and 0.9582 for the HYDICE