# 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  
To demonstrate the effectiveness of the proposed hyperspectral anomaly detection algorithm based on the channel attention mechanism and LRX, we compared it with six existing algorithms: LRX ([7], Background of Joint Sparse Representation (BJSR) [14], LRaSMD [15], Modified Collaborative Representation Detector (MCRD) [29], Auto-Encoder-based (AE-based) [26], and Autonomous hyperspectral anomaly detection method based  
Figure 4: (**a**) The pseudo-color image of Salinas Valley dataset. (**b**) The ground truth. Figure 3: (**a**) The pseudo-color image of AVIRIS airplane dataset. (**b**) The ground truth. on full convolutional auto-encoder (AUTO-AD) [23]. The detection results of all algorithms are evaluated from both qualitative and quantitative perspectives. The brief descriptions of the six compared algorithms are described below. 1. The LRX algorithm is similar to the GRX algorithm. However, the LRX applies a double window sliding strategy to detect anomalies, reducing the effect of the overall background noise [30]. 2. BJSR constructs overcomplete background dictionaries using background samples. It detects anomalies by computing the reconstruction error and estimating adaptive orthogonal background complementary subspaces [16]. 3. LRaSMD decomposes the input image into a low-rank matrix representing the background and a sparse matrix representing the anomaly by low rank decomposition. It constructs the background and calculates the Mahalanobis distance based on the background statistics to detect anomalies [15]. 4. MCRD uses residuals to detect anomalies by removing pixels whose spectrum are significantly different from the majority of pixels in the background, aiming to obtain more representative background pixels [31]. 5. AE-based algorithm employs an encoder-decoder network to extract features and reconstructs the original image. Anomalies are detected by calculating the reconstruction errors [32]. 6. AUTO-AD algorithm reconstructs the background by a fully convolutional auto-encoder with skip connections. Anomaly detection is performed by analyzing the difference between the generated map and the original image [23]. Figure 5 presents a comparison of the ROC curves between the proposed algorithm and the six comparison algorithms. From Figure 5, it can be seen that the ROC curves of the proposed algorithm are more to the upper left corner than those of the other six algorithms, surpassing the curves of the other six algorithms. This indicates that the proposed algorithm exhibits superior anomaly detection accuracy compared to the others. Specifically, in the HYDICE urban dataset and Salinas Valley dataset, the ROC curves of the proposed algorithm are significantly closer to the upper left corner than those of the other algorithms, further highlighting its better anomaly detection performance in these datasets. Although the detection performance of the proposed algorithm in the AVIRIS aircraft dataset is not as obvious as in the other two datasets, its ROC curves still outperform those of the other algorithms. The anomaly detection results for the HYDICE urban dataset visualization are shown in Figure 6. In the visualization, pixels that are closer to yellow indicate a higher probability of being anomalous targets, while pixels closer to blue represent a higher probability of being background pixels. From Figure 6, it can be seen that the LRX, MCRD, and AUTO-AD algorithms have a high miss rate, incorrectly identifying anomalous targets as background pixels. In contrast, the BJSR-, LRaSMD-, and AE-based algorithms demonstrate a high false alarm rate, incorrectly classifying some background pixels as anomalous targets. Both the proposed algorithm and the LRX algorithm achieved better detection performance than the other compared algorithms. The proposed algorithm, which incorporates an channel  
Figure 5: ROC curves of six comparative algorithms and the proposed algorithm on three data sets: (a) HYDICE urban data; (b) AVIRIS airplane data; (c) Salinas scene.