Improvement of unified sample cloud detection technology and its application in GF-6 WFV cloud detection
- Vol. 26, Issue 4, Pages: 646-656(2022)
DOI： 10.11834/jrs.20220352
Quote
扫 描 看 全 文
- About Journal
- Journal Online
- Special Source
摘要
目前基于深度学习的云检测方法，受训练样本限制，算法难以推广及应用。为了快速实现针对多种传感器的高精度的云检测，Sun等（2020）提出统一样本云检测方法。基于AVIRIS高光谱样本库模拟出待检测传感器的云和晴空地表像元，将模拟得到的多光谱样本数据输入到BP神经网络中进行逐像元分类，生成云检测模型，实现Landsat 8 OLI等宽光谱传感器较高精度的云检测。该方法基于统一样本模拟出不同传感器的样本像元库，适用于多种传感器的云检测。由于Landsat 8 OLI波段较多，波谱范围覆盖宽，容易实现云的高精度识别。为了进一步提高其在光谱范围较窄的GF-6 WFV数据上的云检测应用精度，在模拟出的样本库中加入GF-6 WFV数据典型高亮地表像元。通过目视解译对云检测结果进行精度验证，结果表明，该算法利用可见光和近红外通道的遥感数据可以高精度的识别出植被、水体、建筑、裸地等地表类型上空的厚云、碎云和薄云。改进后的云检测算法，云像元平均正确率达到88.40%，在高亮地表上空云像元正确率达到87.40%，在不同地表类型上空的云像元平均正确率为92.60%。结果表明，加入高反射率地物的算法可以利用有限波段实现云和地表的高精度分离。
Abstract
The unified sample cloud detection method proposed by Sun et al. based on the AVIRIS hyperspectral sample database simulates the cloud and clear sky surface pixels of the sensor to be detected. The method also inputs the simulated multi-spectral sample data into BP neural network for pixel-by-pixel classification to generate cloud detection models. This method can realize high-precision cloud detection of Landsat 8 OLI and other wide spectrum sensors. This method, which simulates the sample pixel libraries, is suitable for cloud detection of various sensors. Given that the Landsat 8 OLI sensor has more bands, the spectrum covers a wide range, easily facilitating high-precision cloud identification.,In this paper, an improved cloud detection algorithm is proposed. Owing to the narrow spectral range of GF-6 WFV data and the lack of cloud-sensitive bands such as 1.38 μm, 1.65 μm, and thermal infrared bands, cloud identification accuracy in high reflectivity areas is low. The cloud and clear sky image metadata simulated based on the unified sample pixel database have a weak ability to identify clouds and bright surfaces, and realizing stable and high-precision cloud identification of this type of satellite is impossible. To further improve the application precision of cloud detection on GF-6 WFV data with a narrow spectral range, GF-6 WFV data typical highlighted surface pixels were added into the simulated sample base to realize cloud detection of GF-6 WFV data with high precision.,The main content of the improved unified sample cloud detection algorithm is as follows. (1) GF-6 WFV multi-spectral data simulation. This paper simulates the apparent reflectance of the corresponding band of GF-6 WFV data by the weighted synthesis of the band of AVIRIS hyperspectral data. (2) BP deep learning cloud detection model. In the simulated GF-6 WFV data sample base, typical highlighted surface samples, such as bare land, buildings, and snow, in the GF-6 WFV data are added. The apparent reflectance of each band of the improved data pixel in the sample base is taken as the input vector. The inductive ability of the neural network is used to learn cloud detection rules, generate a cloud detection model, and conduct cloud detection experiments.,The accuracy of cloud detection results was verified by visual interpretation. The artificial labeled cloud area was taken as the reference truth value, and the cloud detection results of the algorithm were compared with the reference truth value on a per-pixel basis. By constructing an error matrix, the precision of cloud inspection results of the proposed algorithm is calculated and verified. With the improved cloud detection algorithm, the average correct rate of cloud pixels reaches 0.884 and 0.874 for cloud pixels over the highlighted land surface and 0.926 for cloud pixels over different land surface types.,The results show that the algorithm can accurately identify the thick clouds, broken clouds, and thin clouds over vegetation, water, buildings, and bare land with high precision by using the remote sensing data of visible and near-infrared channels. The algorithm of adding high-reflectivity ground objects can use limited bands to achieve high-precision separation of clouds and ground.
关键词
云检测GF-6 WFV数据高光谱像元库亮地表深度学习算法
Keywords
cloud detectionGF-6 WFV datahyperspectral pixel librarybright surfacedeep learning algorithm
references
Ball J E, Anderson D T and Chan C S. 2017. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of Applied Remote Sensing, 11(4): 042609 [DOI: 10.1117/1.JRS.11.042609http://dx.doi.org/10.1117/1.JRS.11.042609]
Braaten J D, Cohen W B and Yang Z Q. 2015. Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sensing of Environment, 169: 128-138 [DOI: 10.1016/j.rse.2015.08.006http://dx.doi.org/10.1016/j.rse.2015.08.006]
Carslaw K S, Harrison R G and Kirkby J. 2002. Cosmic rays, clouds, and climate. Science, 298(5599): 1732-1737 [DOI: 10.1126/science.1076964http://dx.doi.org/10.1126/science.1076964]
Chander G, Markham B L and Helder D L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893-903 [DOI: 10.1016/j.rse.2009.01.007http://dx.doi.org/10.1016/j.rse.2009.01.007]
Chen X D, Zhang X, Liu L Y and Wang X F. 2019. Enhanced multi-temporal cloud detection algorithm for optical remote sensing images. National Remote Sensing Bulletin, 23(2): 280-290
陈曦东, 张肖, 刘良云, 汪晓帆. 2019. 增强型多时相云检测. 遥感学报, 23(2): 280-290 [DOI: 10.11834/jrs.20198017http://dx.doi.org/10.11834/jrs.20198017]
Chen Y, Fan R S, Wang J X, Lu W Y, Zhu H and Chu Q Y. 2018. Cloud detection of ZY-3 satellite remote sensing images based on deep learning. Acta Optica Sinica, 38(1): 0128005
陈洋, 范荣双, 王竞雪, 陆婉芸, 朱红, 楚清源. 2018. 基于深度学习的资源三号卫星遥感影像云检测方法. 光学学报, 38(1): 0128005 [DOI: 10.3788/AOS201838.0128005http://dx.doi.org/10.3788/AOS201838.0128005]
Chen Z T, Sun X B and Qiao Y L, 2018. Cloud detection over ocean from PARASOL/POLDER3 satellite data. National Remote Sensing Bulletin, 22(6): 996-1004
陈震霆, 孙晓兵, 乔延利. 2018. PARASOL/POLDER3卫星数据的海洋上空云检测. 遥感学报, 22(6): 996-1004 [DOI: 10.11834/jrs.20187366http://dx.doi.org/10.11834/jrs.20187366]
Clark C. 1999. The detection of ship trail clouds by artificial neural network. International Journal of Remote Sensing, 20(4): 711-726 [DOI: 10.1080/014311699213154http://dx.doi.org/10.1080/014311699213154]
Dan L B and Morrow E. 1998. Evaluation of an AVHRR cloud detection and classification method over the central arctic ocean. Journal of Applied Meteorology and Climatology, 37(2): 166-183 [DOI: 10.1175/1520-0450(1998)037<0166:EOAACD>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1998)037<0166:EOAACD>2.0.CO;2]
Fan X. 2011. Research on coastal area analysis and application based on interpretation of hyperspectral data. Harbin: Harbin Institute of Technology: 4-5
范潇. 2011. 基于高光谱数据解译的近海岸要素分析及应用分析. 哈尔滨: 哈尔滨工业大学: 4-5
Gao J, Wang K, Tian X Y and Chen J. 2018. A BP-NN based cloud detection method for FY-4 remote sensing images. Journal of Infrared and Millimeter Waves, 37(4): 477-485
高军, 王恺, 田晓宇, 陈建. 2018. 基于BP神经网络的风云四号遥感图像云检测算法. 红外与毫米波学报, 37(4): 477-485 [DOI: 10.11972/j.issn.1001-9014.2018.04.016http://dx.doi.org/10.11972/j.issn.1001-9014.2018.04.016]
Goodwin N R, Collett L J, Denham R J, Flood N and Tindall D. 2013. Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM+ time series. Remote Sensing of Environment, 134: 50-65 [DOI: 10.1016/j.rse.2013.02.019http://dx.doi.org/10.1016/j.rse.2013.02.019]
Hagolle O, Huc M, Pascual D V and Dedieu G. 2010. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images. Remote Sensing of Environment, 114(8): 1747-1755 [DOI: 10.1016/j.rse.2010.03.002http://dx.doi.org/10.1016/j.rse.2010.03.002]
Huang J W, Li Z Y, Chen E X, Zhao L and Mo B P. 2021. Classification of plantation types based on WFV multispectral imagery of the GF-6 satellite. National Remote Sensing Bulletin, 25(2): 539-548
黄建文, 李增元, 陈尔学, 赵磊, 莫冰萍. 2021. 高分六号宽幅多光谱数据人工林类型分类. 遥感学报, 25(2): 539-548 [DOI: 10.11834/jrs.20219090http://dx.doi.org/10.11834/jrs.20219090]
Karlsson K G. 1989. Development of an operational cloud classification model. International Journal of Remote Sensing, 10(4/5): 687-693 [DOI: 10.1080/01431168908903910http://dx.doi.org/10.1080/01431168908903910]
Kazantzidis A, Eleftheratos K and Zerefos C S. 2011. Effects of cirrus cloudiness on solar irradiance in four spectral bands. Atmospheric Research, 102(4): 452-459 [DOI: 10.1016/j.atmosres.2011.09.015http://dx.doi.org/10.1016/j.atmosres.2011.09.015]
Kazantzidis A, Tzoumanikas P, Bais A F, Fotopoulos S and Economou G. 2012. Cloud detection and classification with the use of whole-sky ground-based images. Atmospheric Research, 113: 80-88 [DOI: 10.1016/j.atmosres.2012.05.005http://dx.doi.org/10.1016/j.atmosres.2012.05.005]
Lee D S, Storey J C, Choate M J and Hayes R W. 2004. Four years of Landsat-7 on-orbit geometric calibration and performance. IEEE Transactions on Geoscience and Remote Sensing, 42(12): 2786-2795 [DOI: 10.1109/TGRS.2004.836769http://dx.doi.org/10.1109/TGRS.2004.836769]
Li B, Xin X Z, Zhang H L and Hu J C. 2017. Algorithm for cloud shadow identification on complex terrains. National Remote Sensing Bulletin, 21(2): 263-272
李彬, 辛晓洲, 张海龙, 胡继超. 2017. 复杂地形云阴影识别. 遥感学报, 21(2): 263-272 [DOI: 10.11834/jrs.20176079http://dx.doi.org/10.11834/jrs.20176079]
Li Q Y, Lu W T and Yang J. 2011. A hybrid thresholding algorithm for cloud detection on ground-based color images. Journal of Atmospheric and Oceanic Technology, 28(10): 1286-1296 [DOI: 10.1175/JTECH-D-11-00009.1http://dx.doi.org/10.1175/JTECH-D-11-00009.1]
Liu C L and Wu B F. 2004. Application of cloud detection algorithm for the AVHRR data. National Remote Sensing Bulletin, 8(6): 677-687
刘成林, 吴炳方. 2004. NOAA AVHRR云标识技术的应用分析. 遥感学报, 8(6): 677-687 [DOI: 10.11834/jrs.20040619http://dx.doi.org/10.11834/jrs.20040619]
Lv M M, Han L J, Tian S F, Zhou W Q, Li W F and Qian Y G. 2016. Cloud detection under varied surfaces and atmospheric conditions with MODIS imagery. National Remote Sensing Bulletin, 20(6): 1371-1380
吕明明, 韩立建, 田淑芳, 周伟奇, 李伟峰, 钱雨果. 2016. 多样地表和大气状况下的MODIS数据云检测. 遥感学报, 20(6): 1371-1380 [DOI: 10.11834/jrs.20165281http://dx.doi.org/10.11834/jrs.20165281]
Pei L, Liu Y, Tan H and Gao L. 2019. Cloud detection of ZY-3 satellite remote sensing images based on improved fully convolutional neural networks. Laser and Optoelectronics Progress, 56(5): 052801
裴亮, 刘阳, 谭海, 高琳. 2019. 基于改进的全卷积神经网络的资源三号遥感影像云检测. 激光与光电子学进展, 56(5): 052801 [DOI: 10.3788/LOP56.052801http://dx.doi.org/10.3788/LOP56.052801]
Qiu M, Yin H Y, Chen Q and Liu Y J. 2018. Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model. Journal of Computer Applications, 38(11): 3175-3179
邱梦, 尹浩宇, 陈强, 刘颖健. 2018. 基于深度置信网络Otsu混合模型的自动云检测算法. 计算机应用, 38(11): 3175-3179 [DOI: 10.11772/j.issn.1001-9081.2018041350http://dx.doi.org/10.11772/j.issn.1001-9081.2018041350]
Sun L, Yang X, Jia S F, Jia C, Wang Q, Liu X Y, Wei J and Zhou X Y. 2020. Satellite data cloud detection using deep learning supported by hyperspectral data. International Journal of Remote Sensing, 41(4): 1349-1371 [DOI: 10.1080/01431161.2019.1667548http://dx.doi.org/10.1080/01431161.2019.1667548]
Walder P and Maclaren I. 2000. Neural network based methods for cloud classification on AVHRR images. International Journal of Remote Sensing, 21(8): 1693-1708 [DOI: 10.1080/014311600209977http://dx.doi.org/10.1080/014311600209977]
Woodcock C E and Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311-332 [DOI: 10.1016/0034-4257(87)90015-0http://dx.doi.org/10.1016/0034-4257(87)90015-0]
Yao J Q, Chen J Y, Chen Y, Liu C Z and Li G Y. 2019. Cloud detection of remote sensing images based on deep learning and condition random field. Science of Surveying and Mapping, 44(12): 121-127
么嘉棋, 陈继溢, 陈赟, 刘超镇, 李国元. 2019. 联合深度学习和条件随机场的遥感影像云检测. 测绘科学, 44(12): 121-127 [DOI: 10.16251/j.cnki.1009-2307.2019.12.018http://dx.doi.org/10.16251/j.cnki.1009-2307.2019.12.018]
Zhang L, Gong Z N, Wang Q W, Jin D D and Wang X. 2019. Wetlandmapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images. National Remote Sensing Bulletin, 23(2): 313-326
张磊, 宫兆宁, 王启为, 金点点, 汪星. 2019. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取. 遥感学报, 23(2): 313-326 [DOI: 10.11834/jrs.20198083http://dx.doi.org/10.11834/jrs.20198083]
Zheng L J. 2017. Crop Classification Using Multi-features of Chinese Gaofen-1/6 Satellite Remote Sensing Images. Beijing: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences: 2-3
郑利娟. 2017. 基于高分一/六号卫星影像特征的农作物分类研究. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所): 2-3
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources of China
College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China
Sarya Oceanographic Institution, Ocean University of China
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology
National Satellite Ocean Application Service, Ministry of Natural Resources of China
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP