融合多尺度视网膜图像增强的动态云检测

doi:10.13474/j.cnki.11-2246.2022.0326

摘要/Abstract

摘要： 云检测是气象卫星各类定量遥感产品的基础,无论是以云图为基础的天气分析还是以去云为前提的各类大气和地表参数反演、沙尘火情等灾害检测,都需要对遥感影像中的云进行准确识别,尤其是薄云和云边缘等细节识别。针对静止气象卫星(以Himawari-8为例)精细化云检测,本文提出了一种基于多尺度视网膜图像增强的动态云检测算法。该算法基于云层与背景信息辐射特征不同的原理,构建可见光和红外波段的晴空辐射背景场,通过多尺度图像增强和最大类间差方法对辐射差值进行云细节信息的增强和提取。利用2021-2022年的75景MODIS云检测产品作为验证数据进行算法精度验证,整体上算法精度达到91.13%,召回率为94.02%,精确率为86.71%,有较强的适用性和稳健性,且已经较好地支撑了近两年的定量遥感产品业务化应用。

关键词: 云检测, 视网膜, 图像增强, 背景场, 静止卫星

Abstract: Cloud detection is the basis of various quantitative remote sensing products of meteorological satellites. Whether it is weather analysis based on cloud images, inversion of various atmospheric and surface parameters, sand, dust, fire and other disaster detection based on cloud removal, accurate cloud detection in images, especially details such as thin clouds and cloud edges is required. Aiming at the refined cloud detection of geostationary satellites, this paper proposes a dynamic cloud detection method based on multi-scale retinal image enhancement. The clear sky radiation background field is applied to enhance and extract the cloud detail information of the radiation difference through the multi-scale image enhancement and the maximum inter-class difference method. This paper uses 75 MODIS cloud detection products from 2021 to 2022 as the verification data to verify the algorithm accuracy. The overall algorithm accuracy reaches 91.13%, the recall rate is 94.02%, and the precision rate is 86.71%. Overall, the algorithm has strong applicability and robustness. It is excellent and has well supported the commercial application of quantitative remote sensing products in the past two years.

Key words: cloud detection, retinal, image enhancement, background field, geostationary satellites

中图分类号:

P237

陈法融, 房松松. 融合多尺度视网膜图像增强的动态云检测[J]. 测绘通报, 2022, 0(11): 62-66,143.

CHEN Farong, FANG Songsong. Dynamic cloud detection based on multi-scale retinal image enhancement[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 62-66,143.

参考文献

[1] 马芳,张强,郭铌, 等.多通道卫星云图云检测方法的研究[J].大气科学,2007, 31(1):119-128.
[2] DERRIEN M, FARKI B, HARANG L, et al. Automatic cloud detection applied to NOAA-11/AVHRR imagery[J]. Remote Sensing of Environment, 1993, 46(3):246-267.
[3] 裴亮,刘阳,谭海,等.基于改进的全卷积神经网络的资源三号遥感影像云检测[J].激光与光电子学进展,2019,56(5):226-233.
[4] 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.
[5] ROSSOW W B,GARDER L C.Cloud detection using satellite measurements of infrared and visible radiances for ISCCP[J].Climate,1993(a6):2341-2369.
[6] KOTARBA A Z. Evaluation of ISCCP cloud amount with MODIS observations[J]. Atmospheric Research, 2015, 153:310-317.
[7] CAZORLA A, HUSILLOS C, ANTON M, et al. Multi-exposure adaptive threshold technique for cloud detection with sky imagers[J]. Solar Energy, 2015, 114:268-277.
[8] WU T, HU X Y, ZHANG Y, et al. Automatic cloud detection for high resolution satellites stereo images and its application in terrain extraction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 121:143-156.
[9] YU X H, LARY D J. Cloud detection using an ensemble of pixel-based machine learning models incorporating unsupervised classification[J]. Remote Sensing, 2021, 13(16):3289.
[10] 徐少壮,钟来星. 深度学习在遥感影像云检测中的应用[J]. 遥感信息,2021,36(3):129-134.
[11] SCHRÖDER M, BENNARTZ R, SCHVLLER L, et al. Generating cloudmasks in spatial high-resolution observations of clouds using texture and radiance information[J]. International Journal of Remote Sensing, 2002, 23(20):4247-4261.
[12] 陈鹏,张荣,刘政凯.遥感图像云图识别中的特征提取[J]. 中国科学技术大学学报,2009,39(5):484-488.
[13] HUGHES M J, HAYES D J. Automated detection of cloud and cloud shadow in single-date Landsat imagery using neural networks and spatial post-processing[J]. Remote Sensing, 2014, 6(6):4907-4926.
[14] 陈洋,范荣双,王竞雪,等. 基于深度学习的资源三号卫星遥感影像云检测方法[J]. 光学学报,2018,38(1):362-367.
[15] SCHMIT T J, GUNSHOR M M, MENZE I W P, et al. Introducing the next-generation advanced baseline imager on GOES-R[J]. Bulletin of the American Meteorological Society, 2005, 86(8):1079-1096.
[16] 何明元,王毅,俞宏. 地表信息对静止卫星云检测算法的影响[J]. 遥感信息,2019,34(1):15-19.
[17] ZHU Z,WOODCOCK C E. Object-based cloud and cloud shadow detection in Landsat imagery[J].Remote Sensing of Environment,2012,118:83-94.
[18] 王伟轩.基于漫水填充算法提取太湖水边线研究[J].水科学与工程技术,2021(2):57-59.
[19] LAND E H. The Retinex[J]. America Science,1964,52(2):247-264.
[20] JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997,6(7):965-976.