Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder and Low Rank Representation

Bangyong SUN; Zhe ZHAO; Bingliang HU; Tao YU

doi:10.3788/gzxb20215004.0410003

Acta Photonica Sinica, Volume. 50, Issue 4, 254(2021)

Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder and Low Rank Representation

Bangyong SUN1...2, Zhe ZHAO1, Bingliang HU2 and Tao YU2,* |Show fewer author(s)

Author Affiliations

¹College of Printing， Packaging and Digital Media， Xi'an University of Technology， Xi'an70048， China

²Key Laboratory of Spectral Imaging Technology CAS， Xi'an Institute of Optics and Precision Mechanics， Chinese Academy of Sciences， Xi'an710119， China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(17)

Fig. 1. Framework of the proposed method

Download full size

View in Article

Fig. 2. The AVIRIS-1 dataset

Download full size

View in Article

Fig. 3. The AVIRIS-2 dataset

Download full size

View in Article

Fig. 4. The AUC value of two datasets under

Download full size

View in Article

Fig. 5. The AUC value of two datasets under

Download full size

View in Article

Fig. 6. The AUC value of two datasets under different eps and MinSample when p equal 8

Download full size

View in Article

Fig. 7. The AUC value of two datasets under different eps and MinSample when p equal 9

Download full size

View in Article

Fig. 8. The AUC value of two datasets under different eps and MinSample when p equal 10

Download full size

View in Article

Fig. 9. The AUC value of two datasets under different eps and MinSample when p equal 11

Download full size

View in Article

Fig. 10. The AUC value of two datasets under different eps and MinSample when p equal 12

Download full size

View in Article

Fig. 11. The AUC value of two datasets under different p when eps equal 0.012 and MinSample equal 10

Download full size

View in Article

Fig. 12. Detection results of various detection algorithms in AVIRIS-1 dataset

Download full size

View in Article

Fig. 13. Detection results of various detection algorithms in AVIRIS-2 dataset

Download full size

View in Article

Fig. 14. ROC curves of two datasets

Download full size

View in Article

Table 1. The Architecture of 3D-CAE

View table

View in Article

Table 1. The Architecture of 3D-CAE

Block

Layer

Input size

Kernel size

Strides

Output size

Encoder

Conv1

BN+LReLu

189 $\times$ 5 $\times$ 5 $\times$ 1

1 $\times$ 3 $\times$ 3

1 $\times$ 1 $\times$ 1

189 $\times$ 3 $\times$ 3 $\times$ 12

Conv2

BN+LReLu

189 $\times$ 3 $\times$ 3 $\times$ 12

3 $\times$ 1 $\times$ 1

63 $\times$ 3 $\times$ 3 $\times$ 24

Conv3

BN+LReLu

63 $\times$ 3 $\times$ 3 $\times$ 24

1 $\times$ 3 $\times$ 3

1 $\times$ 1 $\times$ 1

63 $\times$ 1 $\times$ 1 $\times$ 36

Conv4

BN+Sigmoid

63 $\times$ 1 $\times$ 1 $\times$ 36

3 $\times$ 1 $\times$ 1

21 $\times$ 1 $\times$ 1 $\times$ 48

Decoder

Deconv1

BN+LReLu

21 $\times$ 1 $\times$ 1 $\times$ 48

3 $\times$ 1 $\times$ 1

63 $\times$ 1 $\times$ 1 $\times$ 36

Deconv2

BN+LReLu

63 $\times$ 1 $\times$ 1 $\times$ 36

1 $\times$ 3 $\times$ 3

1 $\times$ 1 $\times$ 1

63 $\times$ 3 $\times$ 3 $\times$ 24

Deconv3

BN+LReLu

63 $\times$ 3 $\times$ 3 $\times$ 24

3 $\times$ 1 $\times$ 1

189 $\times$ 3 $\times$ 3 $\times$ 12

Deconv4

189 $\times$ 3 $\times$ 3 $\times$ 12

1 $\times$ 3 $\times$ 3

1 $\times$ 1 $\times$ 1

189 $\times$ 5 $\times$ 5 $\times$ 1

Table 2. The AUC value of different methods in two datasets
View table
View in Article
Table 2. The AUC value of different methods in two datasets
Dataset RXW WLRX₄ CRD LRASR DAEAD RC-LRaSMD Proposed
AVIRIS-1
AVIRIS-2
0.911 1 0.944 5 0.972 5 0.989 6 0.977 4 0.989 9 0.993 2
0.940 3 0.967 5 0.951 2 0.909 6 0.957 3 0.990 1 0.991 4

Table 3. Analysis of the results of different steps of the Proposed algorithm
View table
View in Article
Table 3. Analysis of the results of different steps of the Proposed algorithm
Method AVIRIS-1(AUC) AVIRIS-2(AUC)
① 3D-Conv+RX 0.954 0 0.962 2
② 3D-Conv+LRR 0.991 6 0.988 9
③ 3D-Conv+LRR(K-means)+RE 0.990 8 0.990 1
④ 3D-Conv(MSE) +LRR+RE 0.977 2 0.977 4
⑤ 3D-Conv +LRR+RE 0.993 2 0.991 4

Tools

Get Citation

Copy Citation Text

Bangyong SUN, Zhe ZHAO, Bingliang HU, Tao YU. Hyperspectral Anomaly Detection Based on 3D Convolutional Autoencoder and Low Rank Representation[J]. Acta Photonica Sinica, 2021, 50(4): 254

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites