基于改进FCM算法的SDGSAT-1多光谱影像云掩膜生成方法
收稿日期: 2024-04-26
修回日期: 2024-11-11
网络出版日期: 2024-12-23
基金资助
国家自然科学基金(62171435)
Cloud mask production method based on improved FCM algorithm for SDGSAT-1 multispectral images
Received date: 2024-04-26
Revised date: 2024-11-11
Online published: 2024-12-23
可持续发展科学卫星1号（SDGSAT-1）是中国科学院于2021年11月5日成功发射升空的首颗地球科学卫星，旨在实现全球可持续发展目标，为人与自然相互作用的研究提供数据支撑。由于可见光对云层穿透性差，大量光学卫星遥感图像不可避免地受到云层干扰。因此，云掩膜生成是光学遥感处理系统中重要的处理步骤。而SDGSAT-1多光谱影像中缺少传统算法所需要的短波红外波段，传统的云掩膜生成算法难以适用。针对这一问题，提出一种基于改进模糊C均值（FCM）算法的云掩膜生成方法，以亮度、NDWI、NDVI和HOT等4个光谱特征作为输入变量，引入马氏距离，以K-means算法的结果作为初始聚类中心，最后执行FCM算法得到云掩膜。使用已公开的SDGSAT-1多光谱影像对该方法的性能进行验证，并与其他方法进行对比，实验结果表明，该方法的总体精度平均值为95.33%，相比其他方法更具优势。
葛凯强 , 刘佳音 , 王峰 . 基于改进FCM算法的SDGSAT-1多光谱影像云掩膜生成方法[J]. 中国科学院大学学报, 2026 , 43(4) : 531 -540 . DOI: 10.7523/j.ucas.2024.077
Sustainable development science satellite-1 （SDGSAT-1） is the first earth science satellite of the Chinese Academy of Sciences. It was successfully launched on November 5， 2021， aiming to achieve the global sustainable development goals and provide data for the study of interaction between humans and nature. Due to the poor penetration of visible light through cloud， many optical satellite remote sensing images are inevitably disturbed by cloud. Therefore， cloud mask production is an important processing step in optical remote sensing processing system. However， SDGSAT-1 multispectral images lack the short-wave infrared band required by the traditional cloud mask production algorithm， so the traditional cloud mask production algorithm is difficult to apply. Therefore， this paper proposes a cloud mask production method for SDGSAT-1 multispectral images based on fuzzy C-means （FCM） algorithm. The method selects the four spectral features of brightness， NDWI， NDVI， and HOT as input features. On this basis， the Mahalanobis distance is introduced， the results of K-means algorithm are used as the initial clustering center， and finally FCM algorithm is executed to obtain the final cloud mask. In the paper， the published SDGSAT-1 multispectral images are used to verify the performance of the proposed method and to compare it with other methods. The experimental results show that the average accuracy of the proposed method is 95.33%， which is higher than those of other methods.
| [1] | Nguyen T T， Hoang T D， Pham M T， et al. Monitoring agriculture areas with satellite images and deep learning［J］. Applied Soft Computing， 2020， 95： 106565. DOI： 10.1016/j.asoc.2020.106565 . |
| [2] | Lv Z Y， Liu T F， Benediktsson J A， et al. Land cover change detection techniques： very-high-resolution optical images： a review［J］. IEEE Geoscience and Remote Sensing Magazine， 2022， 10（1）： 44-63. DOI： 10.1109/MGRS.2021.3088865 . |
| [3] | Zellweger F， De Frenne P， Lenoir J， et al. Advances in microclimate ecology arising from remote sensing［J］. Trends in Ecology Evolution， 2019， 34（4）： 327-341. DOI： 10.1016/j.tree.2018.12.012 . |
| [4] | Lu X Z， Zeng X， Xu Z， et al. Improving the accuracy of near real-time seismic loss estimation using post-earthquake remote sensing images［J］. Earthquake Spectra， 2018， 34（3）： 1219-1245. DOI： 10.1193/041417eqs072m . |
| [5] | Abdollahi M， Islam T， Gupta A， et al. An advanced forest fire danger forecasting system： integration of remote sensing and historical sources of ignition data［J］. Remote Sensing， 2018， 10（6）： 923. DOI： 10.3390/rs10060923 . |
| [6] | Birk R， Camus W， Valenti E， et al. Synthetic aperture radar imaging systems［J］. IEEE Aerospace and Electronic Systems Magazine， 1995， 10（11）： 15-23. DOI： 10.1109/62.473408 . |
| [7] | Guo H D. Big earth data： a new frontier in Earth and information sciences［J］. Big Earth Data， 2017， 1（1/2）： 4-20. DOI： 10.1080/20964471.2017.1403062 . |
| [8] | 侯舒维， 孙文方， 郑小松. 遥感图像云检测方法综述［J］. 空间电子技术， 2014， 11（3）： 68-76， 86. DOI： 10.3969/j.issn.1674-7135.2014.03.014 . |
| [9] | Irish R R. Landsat 7 automatic cloud cover assessment［C］//Algorithms for Multispectral， Hyperspectral， and Ultraspectral Imagery VI. SPIE， 2000， 4049： 348-355. DOI：10.1117/12.410358 . |
| [10] | Zhu Z， Woodcock C E. Object-based cloud and cloud shadow detection in Landsat imagery［J］. Remote Sensing of Environment， 2012， 118： 83-94. DOI： 10.1016/j.rse.2011.10.028 . |
| [11] | Zhang X， Cui J T， Wang W S， et al. A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm［J］. Sensors （Basel， Switzerland）， 2017， 17（7）： 1474. DOI： 10.3390/s17071474 . |
| [12] | Dong Z P， Wang M， Li D R， et al. Cloud detection method for high resolution remote sensing imagery based on the spectrum and texture of superpixels［J］. Photogrammetric Engineering Remote Sensing， 2019， 85（4）： 257-268. DOI： 10.14358/pers.85.4.257 . |
| [13] | Dinc S， Russell R， Parra L A C. Cloud region segmentation from all sky images using double K-means clustering［C］//2022 IEEE International Symposium on Multimedia （ISM）， Italy. IEEE， 2022： 261-262. DOI： 10.1109/ISM55400.2022.00058 . |
| [14] | Xiang P S. A cloud detection algorithm for MODIS images combining kmeans clustering and otsu method［J］. IOP Conference Series： Materials Science and Engineering， 2018， 392（6）： 062199. DOI： 10.1088/1757-899x/392/6/062199 . |
| [15] | Ping B， Su F Z， Meng Y S. A cloud and cloud shadow detection method based on fuzzy c-means algorithm［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2020， 13： 1714-1727. DOI： 10.1109/JSTARS.2020.2987844 . |
| [16] | Pugazhenthi A， Kumar L S. Cloud extraction from INSAT-3D satellite image by K-means and fuzzy C-means clustering algorithms［C］//2020 International Conference on System， Computation， Automation and Networking （ICSCAN）. Pondicherry， India. IEEE， 2020： 1-4. DOI： 10.1109/ICSCAN49426.2020.9262330 . |
| [17] | Zadeh L A. Fuzzy sets［J］. Information and control， 1965， 8（3）： 338-353. DOI： 10.1016/S0019-9958（65）90241-X . |
| [18] | Guo H D， Chen H Y， Chen L F， et al. Progress on CASEarth satellite development［J］. Chinese Journal of Space Science， 2020， 40（5）： 707-717.DOI：10.11728/cjss2020.05.707 . |
| [19] | McFeeters S K. The use of the normalized difference water index （NDWI） in the delineation of open water features［J］. International Journal of Remote Sensing， 1996， 17（7）： 1425-1432. DOI： 10.1080/01431169608948714 . |
| [20] | Carlson T N， Ripley D A. On the relation between NDVI， fractional vegetation cover， and leaf area index［J］. Remote Sensing of Environment， 1997， 62（3）： 241-252. DOI： 10.1016/S0034-4257（97）00104-1 . |
| [21] | Zhang Y， Guindon B， Cihlar J. An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images［J］. Remote Sensing of Environment， 2002， 82（2/3）： 173-187. DOI： 10.1016/S0034-4257（02）00034-2 . |
| [22] | 许晨， 徐洋， 康雪， 等. 基于模糊C均值聚类算法的去云处理［J］. 河北省科学院学报， 2021， 38（4）： 35-42. DOI： 10.16191/j.cnki.hbkx.2021.04.007 . |
| [23] | 潘聪， 夏斌， 陈彧， 等. 基于模糊聚类的MODIS云检测算法研究［J］. 微计算机信息， 2009， 25（4）： 124-125， 131. |
| [24] | Yang C， Bruzzone L， Sun F Y， et al. A fuzzy-statistics-based affinity propagation technique for clustering in multispectral images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2010， 48（6）： 2647-2659. DOI： 10.1109/TGRS.2010.2040035 . |
| [25] | Celebi M E， Kingravi H A， Vela P A. A comparative study of efficient initialization methods for the K-means clustering algorithm［J］. Expert Systems with Applications， 2013， 40（1）： 200-210. DOI： 10.1016/j.eswa.2012.07.021 . |
| [26] | Braaten J D， Cohen W B， Yang Z Q. Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems［J］. Remote Sensing of Environment， 2015， 169： 128-138. DOI： 10.1016/j.rse.2015.08.006 . |
| [27] | Xiong Q， Wang Y， Liu D Y， et al. A cloud detection approach based on hybrid multispectral features with dynamic thresholds for GF-1 remote sensing images［J］. Remote Sensing， 2020， 12（3）： 450. DOI： 10.3390/rs12030450 . |
| [28] | Davies D L， Bouldin D W. A cluster separation measure［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 1979， PAMI-1（2）： 224-227. DOI： 10.1109/TPAMI.1979.4766909 . |
| [29] | Rousseeuw P J. Silhouettes： a graphical aid to the interpretation and validation of cluster analysis［J］. Journal of Computational and Applied Mathematics， 1987， 20： 53-65. DOI： 10.1016/0377-0427（87）90125-7 . |
/
| 〈 |
|
〉 |
