Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
Abstract
:1. Introduction
- (1)
- The channel attention module is proposed to explore the effective bands of HSIs, enhancing the representation of different land covers in the reconstructed image generated by the auto-encoder network.
- (2)
- The LRX algorithm is employed for anomaly detection on the reconstructed image, which effectively alleviates the impact of existing noises in HSIs.
- (3)
- The effectiveness of the proposed hyperspectral anomaly detection algorithm based on the channel attention model and LRX is verified on three hyperspectral datasets, HYDICE, AVIRIS, and Salinas, with AUC values of 0.9871, 0.9916, and 0.9642, respectively.
2. Related Work
2.1. Auto-Encoder Network
2.2. Channel Attention Mechanism
2.3. LRX Algorithm
3. Hyperspectral Anomaly Detection Based on the Channel Attention Mechanism and LRX
3.1. Channel Attention Network
3.2. Auto-Encoder Network
3.3. LRX-Based Anomaly Detection
4. Experimental Results and Analysis
4.1. Dataset
- (1)
- HYDICE urban dataset: The HYDICE urban dataset is widely used in hyperspectral anomaly detection and is acquired by the hyperspectral digital image acquisition experiment sensor. The original image size is . After removing noise and bands affected by water absorption, 160 bands were retained. In this paper, the original images are cropped to obtain a dataset with the size of . Figure 2 shows the visualization of HYDICE urban data and their corresponding ground truth.
- (2)
- AVIRIS airplane dataset: the AVIRIS aircraft dataset is acquired from the Infrared Imaging Spectrometer in San Diego, CA, USA. The AVIRIS airplane dataset has a spatial resolution of 20 m and a spectral resolution of 10 nm, with a spectral wavelength range from 370 nm to 2510 nm. The AVIRIS aircraft dataset has a spatial size of , with 224 bands. Considering the absorption region, low signal-to-noise ratio and damaged bands, 189 bands are reserved for experiments. Figure 3 illustrates the visualization of the AVIRIS airplane dataset and its corresponding ground truth.
- (3)
- Salinas Valley dataset: The Salinas Valley dataset captures an image of the Salinas Valley in California, USA, taken by the AVIRIS imaging spectrometer. The original image size is , and the dataset is cropped to obtain dimensions of . Figure 4 presents a visual display of the Salinas Valley dataset and its corresponding ground truth.
4.2. Evaluation Criteria
4.3. Comparison with State-of-the-Arts
- (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].
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name | Output Size | Activation Function |
---|---|---|
Input | ||
Dense | RELU | |
Dense | RELU | |
Dense | RELU | |
Dense Z | RELU | |
Dense | RELU | |
Dense | RELU | |
Dense | RELU | |
Output |
Method | Dataset | ||
---|---|---|---|
HYDICE Urban | AVIRIS Airplane | Salinas Scene | |
LRX | |||
BJSR | |||
LRaSMD | |||
MCRD | |||
AE-based | |||
AUTO-AD | |||
Ours |
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Guo, H.; Wang, H.; Song, X.; Ruan, Z. Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX. Appl. Sci. 2023, 13, 6988. https://doi.org/10.3390/app13126988
Guo H, Wang H, Song X, Ruan Z. Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX. Applied Sciences. 2023; 13(12):6988. https://doi.org/10.3390/app13126988
Chicago/Turabian StyleGuo, Huinan, Hua Wang, Xiaodong Song, and Zhongling Ruan. 2023. "Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX" Applied Sciences 13, no. 12: 6988. https://doi.org/10.3390/app13126988
APA StyleGuo, H., Wang, H., Song, X., & Ruan, Z. (2023). Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX. Applied Sciences, 13(12), 6988. https://doi.org/10.3390/app13126988