Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery
Abstract
:1. Introduction
- To change the preconceptions of anomaly detection with insufficient samples, we propose a multi-prior strategy to reliably and adaptively generate prior dictionaries. Specifically, we calculate a series of multi-scale covariance matrices rather than traditional one-order statistics, taking advantage of second-order statistics to naturally model the distribution with integrated spectral and spatial information.
- The twin least-square loss in both the feature and image domains and differential expansion loss are jointly introduced into the architecture to fit the characteristics of high-dimensional and complex HSI data, which can overcome the gradient vanishing and training stability problem.
- To lease the generation ability of the model and reduce the false-alarm rate by an order of magnitude, we design a weakly supervised training pattern to enlarge the distribution diversity between background regions and anomalies, aiming to distinguish between background and anomalies in reconstruction. Experimental results illustrate that the AUC score of in MPN is one order of magnitude lower than other compared methods.
2. Related Work
2.1. Hyperspectral Anomaly Detection
2.2. Generative Adversarial Networks (GANs)
3. Proposed Method
3.1. Network Architecture
3.2. Multi-Prior for Background Construction
3.2.1. Multi-Scale Localizing
3.2.2. Generating Covariance Maps
3.3. Two-Branch Cascaded Architecture with Least-Square Losses
3.3.1. Stability Branch
3.3.2. Separability Branch
3.4. Solving the Cascaded Model
- Minimize by updating parameters of .
- Minimize by updating parameters of .
- Minimize by updating parameters of and the decoder .
4. Experimental Results and Discussion
4.1. Datasets Description
4.1.1. HYDICE
4.1.2. Airport–Beach–Urban (ABU) Database
4.1.3. Grand Island
4.1.4. EI Segundo
4.2. Evaluation Metrics
4.3. Experiment Setup
4.4. Ablation Study
4.5. Discussion
4.5.1. Baseline Methods
4.5.2. Quantitative Comparison
4.5.3. Qualitative Comparison
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
GANs | generative adversarial networks |
HSI | hyperspectral imagery |
MCMs | multi-scale covariance maps |
KNN | K nearest neighbors |
MSE | mean squared error |
ROC | the receiver operating characteristic curve |
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Configuration | HYDICE | Urban-1 | Urban-2 | Grand Island | EI Segundo | Average |
---|---|---|---|---|---|---|
Scene | 0.99716 | 0.99839 | 0.99234 | 0.99940 | 0.98943 | 0.99534 |
Scene | 0.99804 | 0.99805 | 0.99305 | 0.99990 | 0.98816 | 0.99544 |
Scene | 0.99790 | 0.99778 | 0.99302 | 0.99991 | 0.99372 | 0.99654 |
MPN | 0.99945 | 0.99816 | 0.99312 | 0.99991 | 0.99982 | 0.99809 |
Configuration | HYDICE | Urban-1 | Urban-2 | Grand Island | EI Segundo | Average |
---|---|---|---|---|---|---|
Scene | 0.02059 | 0.00193 | 0.00090 | 0.00311 | 0.00207 | 0.00572 |
Scene | 0.02169 | 0.00124 | 0.00159 | 0.00071 | 0.00246 | 0.00554 |
Scene | 0.01965 | 0.00146 | 0.00088 | 0.00257 | 0.00178 | 0.00525 |
MPN | 0.01540 | 0.002 | 0.00145 | 0.00069 | 0.00218 | 0.00518 |
Dataset | MPN | ADLR | LSDM–MoG | RX | AAE | PAB_DC | SAFL |
---|---|---|---|---|---|---|---|
HYDICE | 0.99945 | 0.99471 | 0.95643 | 0.97637 | 0.92185 | 0.99760 | 0.99621 |
Urban-1 | 0.99816 | 0.98774 | 0.99321 | 0.99463 | 0.96806 | 0.98327 | 0.99800 |
Urban-2 | 0.99312 | 0.99284 | 0.97504 | 0.98874 | 0.99284 | 0.54635 | 0.97188 |
Grand Island | 0.99991 | 0.99993 | 0.99989 | 0.99990 | 0.99940 | 0.98768 | 0.99804 |
EI Segundo | 0.99982 | 0.99145 | 0.96798 | 0.97826 | 0.99260 | 0.75911 | 0.99231 |
Average | 0.99809 | 0.99321 | 0.97851 | 0.98757 | 0.97495 | 0.85480 | 0.99141 |
Dataset | MPN | ADLR | LSDM–MoG | RX | AAE | PAB_DC | SAFL |
---|---|---|---|---|---|---|---|
HYDICE | 0.01540 | 0.00316 | 0.18799 | 0.03798 | 0.00962 | 0.10525 | 0.00310 |
Urban-1 | 0.00200 | 0.08011 | 0.04555 | 0.01351 | 0.04229 | 0.16974 | 0.00216 |
Urban-2 | 0.00145 | 0.07451 | 0.01338 | 0.01140 | 0.06192 | 0.32350 | 0.01045 |
Grand Island | 0.00069 | 0.00079 | 0.02848 | 0.00738 | 0.04818 | 0.06799 | 0.01126 |
EI Segundo | 0.00218 | 0.09622 | 0.14127 | 0.00952 | 0.04355 | 0.10506 | 0.01864 |
Average | 0.00518 | 0.05096 | 0.08333 | 0.01596 | 0.0328 | 0.15431 | 0.00912 |
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Zhong, J.; Li, Y.; Xie, W.; Lei, J.; Jia, X. Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery. Remote Sens. 2022, 14, 2859. https://doi.org/10.3390/rs14122859
Zhong J, Li Y, Xie W, Lei J, Jia X. Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery. Remote Sensing. 2022; 14(12):2859. https://doi.org/10.3390/rs14122859
Chicago/Turabian StyleZhong, Jiaping, Yunsong Li, Weiying Xie, Jie Lei, and Xiuping Jia. 2022. "Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery" Remote Sensing 14, no. 12: 2859. https://doi.org/10.3390/rs14122859