Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1428004(2023)
Cloud Detection in Landsat8 OLI Remote Sensing Image with Dual Attention Mechanism
Figures & Tables(24)
Fig. 4. Bottleneck structure. (a) Traditional bottleneck structure; (b) improved bottleneck structure
Fig. 11. Ratio of image blocks covered by thick and thin clouds in different datasets
Fig. 12. Detection results of thin and thick clouds over land and coastal areas. (a) Original composite image (land); (b) cloud detection results of proposed algorithm (land); (c) original composite image (ocean); (d) cloud detection results of proposed algorithm (ocean)
Fig. 13. Detection results of different cloud detection methods in scenario 1. (a) Original composite image; (b) real image of the ground; (c) F-CNN algorithm; (d) proposed algorithm+no multi-scale; (e) RF algorithm; (f) SVM algorithm; (g) proposed algorithm; (h) Fmask algorithm
Fig. 14. Detection results of different cloud detection methods in scenario 2. (a) Original composite image; (b) real image of the ground; (c) F-CNN algorithm; (d) proposed algorithm+no multi-scale; (e) RF algorithm; (f) SVM algorithm; (g) proposed algorithm; (h) Fmask algorithm
Fig. 15. Detection results of different cloud detection methods in scenario 3. (a) Original composite image; (b) real image of the ground; (c) F-CNN algorithm; (d) proposed algorithm+no multi-scale; (e) RF algorithm; (f) SVM algorithm; (g) proposed algorithm; (h) Fmask algorithm
Fig. 16. Test datasets cloud detection accuracy distribution map. (a) Comparison with F-CNN model; (b) comparison with self-contrast model; (c) comparison with RF model; (d) comparison with SVM model
Table 1. Image distribution of cloud coverage in train and test datasets
View tableTable 1. Image distribution of cloud coverage in train and test datasets
Cloud cover rate /% Train set Train set ratio /% Test set Test set ratio /% 0 294 23.8 311 38.6 （0，10］ 300 24.2 177 22.0 （10，20］ 164 13.3 80 9.9 （20，30］ 155 12.6 65 8.0 （30，40］ 113 9.1 60 7.5 （40，100］ 210 17.0 113 14.0
Table 2. Detection performance of different methods for thick and thin clouds
View tableTable 2. Detection performance of different methods for thick and thin clouds
Method Precision_c Recall_c F_Score_c Precision_t Recall_t F_Score_t Time /s SVM 0.8648 0.7984 0.8303 0.7409 0.5879 0.6556 7.8 RF 0.8628 0.8194 0.8406 0.7396 0.5923 0.6578 10.6 F-CNN 0.8701 0.7620 0.8125 0.7011 0.5943 0.6433 13.7 self-contrast 0.8666 0.6461 0.7403 0.5899 0.4784 0.5283 16.8 Proposed method 0.9074 0.8946 0.8920 0.7813 0.7693 0.7753 9.2
Table 3. Full cloud detection performance of different methods
View tableTable 3. Full cloud detection performance of different methods
Method RR ER FAR RER SVM 0.8340 0.0549 0.0517 15.19 RF 0.8440 0.0601 0.0275 14.05 FCNN 0.8236 0.0637 0.0371 12.93 self-contrast 0.7361 0.0887 0.0290 8.30 Fmask 0.9923 0.1049 0.6341 9.46 Proposed method 0.9340 0.0385 0.0693 24.22
Table 4. Ablation experiment results
View tableTable 4. Ablation experiment results
Type RR ER Time /s A 0.793 0.0691 12.7 B 0.802 0.0709 13.3 C 0.890 0.0777 11.2
Tools
Get Citation
Copy Citation Text
Hao Wan, Lei Lei, Rui Li, Wei Chen, Yiqing Shi. Cloud Detection in Landsat8 OLI Remote Sensing Image with Dual Attention Mechanism[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1428004
Paper Information
Category: Remote Sensing and Sensors
Received: Mar. 21, 2022
Accepted: Aug. 4, 2022
Published Online: Jul. 17, 2023
The Author Email: Lei Lei (7468356@qq.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20