CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection
"> Figure 1
<p>Flowchart of proposed CD-SDN.</p> "> Figure 2
<p>Architecture of the proposed SDN. (<b>a</b>) FCN; (<b>b</b>) USNet; and (<b>c</b>) CSNet.</p> "> Figure 3
<p>Demonstration of pixel distribution (<b>a</b>) before L-SFA, and (<b>b</b>) after L-SFA.</p> "> Figure 4
<p>Process of re-estimating the argument region. (<b>a</b>): USNet&CSNet-BCMs; (<b>b</b>): Argument Region; (<b>c1</b>): CPMAs; (<b>c2</b>): AL-CPM; (<b>d1</b>): AM-CPM; (<b>d2</b>): AL-BCM; (<b>e1</b>): AM-BCM.</p> "> Figure 5
<p>Visualization comparison of segmentation results by using discouraged subprocess and encouraged subprocess: (<b>a</b>) discouraged subprocess by merging AM-CPM and AL-CPM with USNet-CPM, before applying the K-means; (<b>b</b>) applying K-means to AM-CPM and AL-CPM; (<b>c</b>) HAM; (<b>d</b>) encouraged subprocess by applying K-means to AM-CPM and AL-CPM, before merging the results with HAM. Chg: changed pixels; Un: unchanged pixels; Chg-A: changed pixels of argument region; Un-A: unchanged pixels of argument region.</p> "> Figure 6
<p>Tested scene images and the corresponding results in the process. (<b>R1</b>) ‘Santa Barbara’, (<b>R2</b>) ‘Hermiston’, and (<b>R3</b>) ‘Bay Area’. (<b>C1</b>) Reference image, (<b>C2</b>) Query image, (<b>C3</b>) Ground truth map, (<b>C4</b>) USNet-CPM, (<b>C5</b>) CSNet-CPM, (<b>C6</b>) USNet-BCM, (<b>C7</b>) CSNet-BCM, and (<b>C8</b>) Argument region.</p> "> Figure 7
<p>Re-estimation of argument regions using AM. (<b>R1</b>) ‘Santa Barbara’, (<b>R2</b>) ‘Hermiston’, and (<b>R3</b>) ‘Bay Area’; (<b>C1</b>) USNet-CPMA, (<b>C2</b>) CSNet-CPMA, (<b>C3</b>) AM-CPM, (<b>C4</b>) AM-BCM, (<b>C5</b>) BCM, (<b>C6</b>) F-HSM, and (<b>C7</b>) K-HSM.</p> "> Figure 8
<p>Re-estimation of argument regions using AL. (<b>R1</b>) ‘Santa Barbara’, (<b>R2</b>) ‘Hermiston’, and (<b>R3</b>) ‘Bay Area’; (<b>C1</b>) AL-CPM, (<b>C2</b>) AL-BCM, (<b>C3</b>) detected BCM, (<b>C4</b>) F-HSM, and (<b>C5</b>) K-HSM.</p> "> Figure 9
<p>Visualization of comparison on dataset ‘Santa Barbara’. (<b>C1</b>) CVA, (<b>C2</b>) CD-FCN, (<b>C3</b>) DSFA, (<b>C4</b>) CD-USNet, (<b>C5</b>) CD-CSNet, (<b>C6</b>) CD-SDN-AM, (<b>C7</b>) CD-SDN-AL; (<b>R1</b>) CPM, (<b>R2</b>) BCM, (<b>R3</b>) F-HSM, (<b>R4</b>) K-HSM.</p> "> Figure 10
<p>Visualization of comparison on dataset ‘Hermiston’. (<b>C1</b>) CVA, (<b>C2</b>) CD-FCN, (<b>C3</b>) DSFA, (<b>C4</b>) CD-USNet, (<b>C5</b>) CD-CSNet, (<b>C6</b>) CD-SDN-AM, (<b>C7</b>) CD-SDN-AL. (<b>R1</b>) CPM, (<b>R2</b>) BCM, (<b>R3</b>) HSM.</p> "> Figure 11
<p>Visualization of comparison on dataset ‘Bay Area’. (<b>C1</b>) CVA, (<b>C2</b>) CD-FCN, (<b>C3</b>) DSFA, (<b>C4</b>) CD-USNet, (<b>C5</b>) CD-CSNet, (<b>C6</b>) CD-SDN-AM, (<b>C7</b>) CD-SDN-AL. (<b>R1</b>) CPM, (<b>R2</b>) BCM, (<b>R3</b>) F-HSM, (<b>R4</b>) K-HSM.</p> ">
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Generation of CPM & BCM
3.2. Generation of BAM & Re-Estimation of Argument Region
Algorithm 1 The executive process of proposed CD-SDN to generate the final binary change map |
Input: Bi-temporal images R and Q; |
Output: The final binary change map BCM1 and BCM2; |
1: Utilized pre-detected BCM as a reference to select training samples X and Y; |
2: Initialization for parameters of SDN as ; |
3: while i < epochs do |
4: Compute the bi-temporal projective features of training samples: |
= f (X, ) and = f (Y, ); |
5: Compute the gradient of Loss function = |
by ∂L ( )/∂ and ∂L ( )/∂; |
6: Update the parameters; |
7: i++; |
8: end |
9: Training model is used to generate the projective features and of whole bi-temporal images R and Q; |
10: Apply L-SFA to the and for post-processing to get and ; |
11: Euclidean distance to calculate the CPM; |
12: K-means to get the USNet-BCM and CSNet-BCM; |
13: High-Gain to generate the BAM and HAM; |
14: Case AM approach: |
15: Calculate the CPMA-USNet and CPMA-CSNet by using BAM; |
16: Arithmetic mean to get the AM-CPM; |
17: K-mean to generate the AM-BCM; |
18: Merge the AM-BCM with HAM to obtain the final BCM1; |
19: Case AL approach: |
20: Argument learning to generate the projective features; |
21: Euclidean distance to calculate the AL-CPM; |
22: K-means is to generate the AL-BCM; |
23: Merge the AL-BCM with HAM to obtain the final BCM2; |
24: return BCM1, BCM2; |
4. Results
4.1. Performance of Proposed CD-SDN
4.2. Comparison with State-of-the-Art Works
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Methods | CVA [8] | CD-FCN [20] | DSFA [20] | CD-USNet [21] | CD-CSNet | CD-SDN-AM | CD-SDN-AL |
---|---|---|---|---|---|---|---|
(Proposed) | (Proposed) | (Proposed) | |||||
TP | 42,247 | 44,435 | 43,426 | 43,442 | 45,497 | 46,532 | 45,481 |
TN | 68,093 | 77,257 | 79,333 | 79,580 | 77,087 | 76,838 | 78,892 |
FP | 12,325 | 3161 | 1085 | 838 | 3331 | 3580 | 1526 |
FN | 9887 | 7699 | 8708 | 8692 | 6637 | 5602 | 6653 |
OA_CHG | 0.8104 | 0.8523 | 0.8330 | 0.8333 | 0.8727 | 0.8925 | 0.8724 |
OA_UN | 0.8467 | 0.9607 | 0.9865 | 0.9896 | 0.9586 | 0.9555 | 0.9810 |
OA | 0.8324 | 0.9181 | 0.9261 | 0.9281 | 0.9248 | 0.9307 | 0.9383 |
Kappa | 0.6517 | 0.8257 | 0.8411 | 0.8452 | 0.8406 | 0.8539 | 0.8684 |
F1 | 0.7918 | 0.8911 | 0.8987 | 0.9012 | 0.9013 | 0.9102 | 0.9175 |
Methods | CVA [8] | CD-FCN [20] | DSFA [20] | CD-USNet [21] | CD-CSNet | CD-SDN-AM | CD-SDN-AL |
---|---|---|---|---|---|---|---|
(Proposed) | (Proposed) | (Proposed) | |||||
TP | 9338 | 9284 | 9299 | 9206 | 9314 | 9195 | 9346 |
TN | 66,953 | 67,452 | 67,467 | 67,650 | 67,535 | 67,674 | 67,632 |
FP | 1061 | 562 | 547 | 364 | 479 | 340 | 382 |
FN | 648 | 702 | 687 | 780 | 672 | 791 | 640 |
OA_CHG | 0.9351 | 0.9297 | 0.9312 | 0.9219 | 0.9327 | 0.9208 | 0.9359 |
OA_UN | 0.9844 | 0.9917 | 0.9920 | 0.9946 | 0.9930 | 0.9950 | 0.9944 |
OA | 0.9781 | 0.9838 | 0.9842 | 0.9853 | 0.9852 | 0.9855 | 0.9869 |
Kappa | 0.9036 | 0.9270 | 0.9287 | 0.9331 | 0.9334 | 0.9338 | 0.9407 |
F1 | 0.9162 | 0.9363 | 0.9378 | 0.9415 | 0.9418 | 0.9421 | 0.9482 |
Methods | CVA [8] | CD-FCN [20] | DSFA [20] | CD-USNet [21] | CD-CSNet | CD-SDN-AM | CD-SDN-AL |
---|---|---|---|---|---|---|---|
(Proposed) | (Proposed) | (Proposed) | |||||
TP | 29,521 | 32,837 | 32,972 | 33,227 | 33,738 | 35,717 | 35,632 |
TN | 32,259 | 33,016 | 32,961 | 32,761 | 32,603 | 31,815 | 32,422 |
FP | 1952 | 1195 | 1250 | 1450 | 1608 | 2396 | 1789 |
FN | 9749 | 6433 | 6298 | 6043 | 5532 | 3553 | 3638 |
OA_CHG | 0.7517 | 0.8362 | 0.8396 | 0.8461 | 0.8591 | 0.9095 | 0.9074 |
OA_UN | 0.9429 | 0.9651 | 0.9635 | 0.9576 | 0.9530 | 0.9300 | 0.9477 |
OA | 0.8408 | 0.8962 | 0.8973 | 0.8980 | 0.9028 | 0.9190 | 0.9261 |
Kappa | 0.6846 | 0.7934 | 0.7955 | 0.7968 | 0.8062 | 0.8377 | 0.8521 |
F1 | 0.8346 | 0.8959 | 0.8973 | 0.8987 | 0.9043 | 0.9231 | 0.9292 |
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Li, J.; Yuan, X.; Li, J.; Huang, G.; Li, P.; Feng, L. CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection. Remote Sens. 2022, 14, 4806. https://doi.org/10.3390/rs14194806
Li J, Yuan X, Li J, Huang G, Li P, Feng L. CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection. Remote Sensing. 2022; 14(19):4806. https://doi.org/10.3390/rs14194806
Chicago/Turabian StyleLi, Jinlong, Xiaochen Yuan, Jinfeng Li, Guoheng Huang, Ping Li, and Li Feng. 2022. "CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection" Remote Sensing 14, no. 19: 4806. https://doi.org/10.3390/rs14194806