| Citation: |
DAI Peiyu, LI Shizhong, JI Shunping, REN Ni. A Cloud Detection Method with Domain Adaptation Enhanced Generalization Capability[J]. Geomatics and Information Science of Wuhan University, 2025, 50(1): 110-119. DOI: 10.13203/j.whugis20220435
|
Due to the differences in spectral range, imaging conditions and imaging time of remote sensing sensors, there are usually color and spectral differences between remote sensing images obtained by different sensors. Therefore, it is difficult to directly apply the model pretrained with open-source datasets for the cloud detection task on current target images.
A domain adaptive cloud detection algorithm is proposed, which realizes the spectral mapping between the target remote sensing images and the images of existing training datasets, and improves the generalization ability and robustness of the pretrained deep learning cloud detection model in practical applications. Specifically, the domain adaptation based cloud detection framework uses a convolutional neural network based cloud detection model as the prediction algorithm, and CycleGAN as the spectral domain transfer between images in the source domain and the target domain.
The cross-domain experiments among Gaofen‑2, land satellite (Landsat) 7, Landsat 8 have proved the effectiveness and advance of the proposed method. (1) When the test dataset and training dataset are obtained from the same sensor, deep learning-based cloud detection algorithms obtain better results than the traditional cloud detection algorithms. (2) When cloud detection models trained on images from the source dataset are directly used to detect test images of the target dataset, the accuracy decreases obviously. (3) When the test images are pre-transformed by the introduced domain transfer algorithm, the accuracy of the cloud detection results is significantly improved and much better than the traditional methods. In addition, we discovered that when the spectral range of source training images covers the spectral range of test images, the accuracy of the cloud detection results decreases only slightly, but in the opposite case, the accuracy of the cloud detection results decreases significantly.
Domain transfer can map images between the test dataset and the training dataset at the pixel level, so that the distributions of them are similar both in spectral and spatial space. By introducing domain adaptation, the deep learning-based cloud detection method can make full use of the pre-trained models from existing datasets, which greatly reduces the demand on preparation of target labels.
| [1] |
ZHANG Y C, ROSSOW W B, LACIS A A, et al. Calculation of Radiative Fluxes from the Surface to Top of Atmosphere Based on ISCCP and Other Global Data Sets： Refinements of the Radiative Transfer Model and the Input Data[J]. Journal of Geophysical Research （Atmospheres）, 2004, 109（D19）： D19105.
|
| [2] |
MINNIS P, TREPTE Q Z, SUN-MACK S, et al. Cloud Detection in Nonpolar Regions for CERES Using TRMM VIRS and Terra and Aqua MODIS Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46（11）： 3857-3884.
|
| [3] |
BIAN J H, LI A N, LIU Q N, et al. Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context[J].Remote Sensing,2016,8（1）： 31.
|
| [4] |
ZHU X L, HELMER E H. An Automatic Method for Screening Clouds and Cloud Shadows in Optical Satellite Image Time Series in Cloudy Regions[J]. Remote Sensing of Environment, 2018, 214： 135-153.
|
| [5] |
GOODWIN N R, COLLETT L J, DENHAM R J, et al. Cloud and Cloud Shadow Screening Across Queensland, Australia： An Automated Method for Landsat TM/ETM+ Time Series[J]. Remote Sensing of Environment, 2013, 134： 50-65.
|
| [6] |
IRISH R R, BARKER J L, GOWARD S N, et al. Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment （ACCA） Algorithm[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72（10）： 1179-1188.
|
| [7] |
康一飞, 潘励, 孙明伟, 等. 基于高斯混合模型法的国产高分辨率卫星影像云检测[J]. 武汉大学学报（信息科学版）, 2017, 42（6）： 782-788.
KANG Yifei, PAN Li, SUN Mingwei, et al. Gaussian Mixture Model Based Cloud Detection for Chinese High Resolution Satellite Imagery[J]. Geomatics and Information Science of Wuhan University, 2017, 42（6）： 782-788.
|
| [8] |
文雄飞, 董新奕, 刘良明. “云指数法” 云检测研究[J]. 武汉大学学报（信息科学版）, 2009, 34（7）： 838-841.
WEN Xiongfei, DONG Xinyi, LIU Liangming. Cloud Index Method for Cloud Detection[J]. Geomatics and Information Science of Wuhan University, 2009, 34（7）： 838-841.
|
| [9] |
秦永, 付仲良, 周凡, 等. 一种利用TM影像自动检测云层的方法[J]. 武汉大学学报（信息科学版）, 2014, 39（2）： 234-238.
QIN Yong, FU Zhongliang, ZHOU Fan, et al. A Method of Automatic Cloud Detection Using TM Image[J]. Geomatics and Information Science of Wuhan University, 2014, 39（2）： 234-238.
|
| [10] |
DI VITTORIO A V, EMERY W J. An Automated, Dynamic Threshold Cloud-Masking Algorithm for Daytime AVHRR Images over Land[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40（8）： 1682-1694.
|
| [11] |
LUO Y, TRISHCHENKO A P, KHLOPENKOV K V. Developing Clear-Sky, Cloud and Cloud Shadow Mask for Producing Clear-Sky Composites at 250-meter Spatial Resolution for the Seven MODIS Land Bands over Canada and North America[J].Remote Sensing of Environment, 2008, 112（12）： 4167-4185.
|
| [12] |
MELGANI F. Contextual Reconstruction of Cloud-Contaminated Multitemporal Multispectral Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44（2）： 442-455.
|
| [13] |
FISHER A. Cloud and Cloud-Shadow Detection in SPOT5 HRG Imagery with Automated Morphological Feature Extraction[J].Remote Sensing,2014,6（1）： 776-800.
|
| [14] |
李微, 方圣辉, 佃袁勇, 等. 基于光谱分析的MODIS云检测算法研究[J]. 武汉大学学报（信息科学版）, 2005, 30（5）： 435-438.
LI Wei, FANG Shenghui, DIAN Yuanyong, et al. Cloud Detection in MODIS Data Based on Spectrum Analysis[J]. Geomatics and Information Science of Wuhan University, 2005, 30（5）： 435-438.
|
| [15] |
ZHU Z, WOODCOCK C E. Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery[J]. Remote Sensing of Environment, 2012, 118： 83-94.
|
| [16] |
QIU S, HE B B, ZHU Z, et al. Improving Fmask Cloud and Cloud Shadow Detection in Mountainous Area for Landsats 4-8 Images[J]. Remote Sensing of Environment, 2017, 199： 107-119.
|
| [17] |
FRANTZ D, HAß E, UHL A, et al. Improvement of the Fmask Algorithm for Sentinel-2 Images： Separating Clouds from Bright Surfaces Based on Parallax Effects[J].Remote Sensing of Environment,2018, 215： 471-481.
|
| [18] |
QIU S, ZHU Z, HE B B. Fmask 4.0： Improved Cloud and Cloud Shadow Detection in Landsats 4–8 and Sentinel-2 Imagery[J]. Remote Sensing of Environment, 2019, 231： 111205.
|
| [19] |
LI Z W, SHEN H F, LI H F, et al. Multi-feature Combined Cloud and Cloud Shadow Detection in GaoFen-1 Wide Field of View Imagery[J]. Remote Sensing of Environment, 2017, 191： 342-358.
|
| [20] |
MATEO-GARCÍA G, GÓMEZ-CHOVA L, CAMPS-VALLS G. Convolutional Neural Networks for Multispectral Image Cloud Masking[C]//IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, USA, 2017.
|
| [21] |
XIE F Y, SHI M Y, SHI Z W, et al. Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10（8）： 3631-3640.
|
| [22] |
LI Z W, SHEN H F, WEI Y C, et al. Cloud Detection by Fusing Multi-scale Convolutional Features[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences,2018,4（3）： 149-152.
|
| [23] |
YANG J Y, GUO J H, YUE H J, et al. CDnet： CNN-Based Cloud Detection for Remote Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57（8）： 6195-6211.
|
| [24] |
GUO J H, YANG J Y, YUE H J, et al. CDnetV2： CNN-Based Cloud Detection for Remote Sensing Imagery with Cloud-Snow Coexistence[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59（1）： 700-713.
|
| [25] |
SHAO Z F, PAN Y, DIAO C Y, et al. Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57（6）： 4062-4076.
|
| [26] |
LI Y S, CHEN W, ZHANG Y J, et al. Accurate Cloud Detection in High-Resolution Remote Sensing Imagery by Weakly Supervised Deep Learning[J]. Remote Sensing of Environment, 2020, 250： 112045.
|
| [27] |
JI S P, DAI P Y, LU M, et al. Simultaneous Cloud Detection and Removal from Bitemporal Remote Sensing Images Using Cascade Convolutional Neural Networks[J].IEEE Transactions on Geoscien‑ce and Remote Sensing, 2021, 59（1）： 732-748.
|
| [28] |
LUO C, FENG S S, YANG X F, et al. LWCDnet： A Lightweight Network for Efficient Cloud Detection in Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60： 5409816.
|
| [29] |
LI W W, ZHANG F, LIN H, et al. Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60： 4107117.
|
| [30] |
WIELAND M, LI Y, MARTINIS S. Multi-sensor Cloud and Cloud Shadow Segmentation with a Convolutional Neural Network[J]. Remote Sensing of Environment, 2019, 230： 111203.
|
| [31] |
MATEO-GARCÍA G,LAPARRA V,LÓPEZ-PUIGDOLLERS D, et al. Transferring Deep Learning Models for Cloud Detection Between Landsat-8 and Proba-V[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160： 1-17.
|
| [32] |
ZHU J Y, PARK T, ISOLA P, et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks[C]//IEEE International Conference on Computer Vision, Venice, Italy, 2017.
|
| [33] |
FOGA S, SCARAMUZZA P L, GUO S, et al. Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products[J]. Remote Sensing of Environment, 2017, 194： 379-390.
|
| [34] |
RONNEBERGER O, FISCHER P, BROX T. U-Net： Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015.
|
| [35] |
TSAI Y H, HUNG W C, SCHULTER S, et al. Learning to Adapt Structured Output Space for Semantic Segmentation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition,Salt Lake City, USA,2018.
|
| [1] | GAO Xianjun, RAN Shuhao, ZHANG Guangbin, YANG Yuanwei. Building Extraction Based on Multi-feature Fusion and Object-Boundary Joint Constraint Network[J]. Geomatics and Information Science of Wuhan University, 2024, 49(3): 355-365. DOI: 10.13203/j.whugis20210520 |
| [2] | ZENG Anmin, MING Feng, WU Fumei. Fusion Model for Long-Term Solutions to the Terrestrial Reference Frame Using Internal Constraints[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1447-1451. DOI: 10.13203/j.whugis20190453 |
| [3] | LI Weilian, ZHU Jun, ZHANG Yunhao, FU Lin, HU Ya, YIN Lingzhi, DAI Yi. A Fusion Modeling and Interaction Method with Spatial Semantic Constraint for Debris Flow VR Scene[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 1073-1081. DOI: 10.13203/j.whugis20180329 |
| [4] | XIE Xuemei, SONG Yingchun, XIA Yuguo. An Active Set Algorithm of Conjugate Gradients for Adjustment Model with Interval Constraints[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9): 1274-1281. DOI: 10.13203/j.whugis20170325 |
| [5] | FAN Yaxin, ZHU Xinyan, GUO Wei, SHE Bing. Boundary-Constrained Max-p-Regions Problem and Its Heuristic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6): 859-865. DOI: 10.13203/j.whugis20170253 |
| [6] | XIE Xuemei, SONG Yingchun, XIAO Zhaobing. A Fast Search Algorithm in Adjustment Model with Inequality Constraint[J]. Geomatics and Information Science of Wuhan University, 2018, 43(9): 1349-1354. DOI: 10.13203/j.whugis20160435 |
| [7] | YANG Yuanxi, ZENG Anmin, JING Yifan. GNSS Data Fusion with Functional and Stochastic ModelConstraints as well as Property Analysis[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 127-131. DOI: 10.13203/j.whugis20130378 |
| [8] | ZHU Qing, LI Haifeng, YANG Xiaoxia. Hierarchical Semantic Constraint Model for Focused Remote Sensing Information Services[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12): 1454-1457. |
| [9] | ZENG Anmin, YANG Yuanxi, OUYANG Guichong. Sequential Adjustment with Constraints Among Parameters[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 183-186. |
| [10] | ZHONG Min, YAN Haoming, ZHU Yaozhong, YU Yongqiang. Global Ocean Angular Momentum Variability and Geodetic Constraint[J]. Geomatics and Information Science of Wuhan University, 2003, 28(6): 697-702. |
| 1. |
任亚飞，郑玉丽，姚雷博. 基于图像识别的淬火过程中钢球计数研究. 拖拉机与农用运输车. 2021(06): 52-54+58 .
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: