A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended MultiAttribute Profile
Abstract
:1. Introduction
2. Related Work
2.1. LowRank and Sparse Matrix Decomposition
2.2. Extended Morphological Attribute Profile
3. Proposed Method
3.1. GBSAED Method Flowchart
3.2. Fast Extraction of Abnormal Spectral Features Using Greedy Bilateral Smoothing
3.3. Extracting Abnormal Spatial Features Based on the Extended MultiAttribute Profile
3.4. Proposed GBSAED Algorithm
Algorithm 1. GBSAED framework for hyperspectral anomaly detection 
Input: Hyperspectral image; rank $r$ rank step $\u2206r$; power $k$; soft thresholding $\lambda $; tolerance $\tau $; 
Output: A twodimensional detection result.

4. Experimental Results and Analysis
4.1. Experiment Setup
4.2. Hyperspectral Datasets
4.3. Detection Performance
4.4. Parameter Setting Considerations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Detector  Parameter  Texas Coast  Belcher Bay  PaviaC  San Diego  Xiong’an 

LRX  (W_{in}, W_{out})  (19, 21)  (19, 21)  (7, 9)  (11, 25)  (19, 21) 
KRX  W_{length}  9  9  9  9  5 
Kernel function  Polynomial  Polynomial  Polynomial  Polynomial  Polynomial  
Kernel variance  1  0.6  1.6  0.1  3  
CRD  (W_{in}, W_{out})  (5, 9)  (15, 17)  (7, 9)  (15, 17)  (15, 17) 
GTVLRR  Beta  1000  0.01  1000  0.01  100 
Lambda  0.01  0.01  0.001  0.1  0.1  
Gamma  0.01  0.001  0.01  0.01  0.01  
LSMAD  r  1  3  1  2  3 
Card  3  1  1  1  5  
Power  1  9  1  10  1  
PRLRaSAD  r  2  2  1  2  2 
Card  0.21  0.25  0.475  0.125  0.272  
a  100  100  100  100  100  
GBSAED  r  1  3  1  2  3 
$\lambda $  1  20  1  20  30  
K  1  1  1  10  5  
$\tau $  0.001  0.001  0.001  0.001  0.001  
$\u2206r$  1  1  1  1  1 
Detector  Texas Coast  Belcher Bay  PaviaC  San Diego  Xiong’an  Average (All Scenes) 

RX  0.9946  0.9617  0.9984  0.9112  0.9026  0.9537 
LRX  0.9463  0.9975  0.9430  0.9461  0.9276  0.9521 
SSRX  0.9801  0.9723  0.9877  0.9918  0.4482  0.8760 
KRX  0.9938  0.9802  0.9993  0.979  0.9469  0.9798 
CRD  0.9460  0.9929  0.9894  0.9821  0.9400  0.9701 
GTVLRR  0.9799  0.9670  0.9975  0.9090  0.9402  0.9587 
LSMAD  0.9988  0.9914  0.9998  0.9936  0.9735  0.9914 
PRLRaSAD  0.9978  0.9057  0.9998  0.9964  0.9757  0.9751 
GBSAED  0.9993  0.9999  0.9998  0.9993  0.9840  0.9965 
Detector  Texas Coast  Belcher Bay  PaviaC  San Diego  Xiong’an  Average (All Scenes) 

RX  0.094  0.154  0.051  0.079  0.138  0.103 
LRX  38.937  49.081  8.417  49.263  83.620  45.864 
SSRX  0.159  0.232  0.098  0.166  0.208  0.173 
KRX  21.751  49.611  19.447  23.059  6.389  24.051 
CRD  5.943  17.357  2.618  7.539  12.678  9.227 
GTVLRR  127.234  278.054  95.241  96.602  212.809  161.988 
LSMAD  8.527  17.018  4.380  7.847  14.190  10.392 
PRLRaSAD  15.413  16.428  5.362  9.321  18.952  13.059 
GBSAED  0.115  0.243  0.118  0.117  0.165  0.152 
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Liu, S.; Zhang, L.; Cen, Y.; Chen, L.; Wang, Y. A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended MultiAttribute Profile. Remote Sens. 2021, 13, 3954. https://doi.org/10.3390/rs13193954
Liu S, Zhang L, Cen Y, Chen L, Wang Y. A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended MultiAttribute Profile. Remote Sensing. 2021; 13(19):3954. https://doi.org/10.3390/rs13193954
Chicago/Turabian StyleLiu, Senhao, Lifu Zhang, Yi Cen, Likun Chen, and Yibo Wang. 2021. "A Fast Hyperspectral Anomaly Detection Algorithm Based on Greedy Bilateral Smoothing and Extended MultiAttribute Profile" Remote Sensing 13, no. 19: 3954. https://doi.org/10.3390/rs13193954