Classification and detection of cloud, snow and fog in remote sensing images based on random forest

doi:10.6046/gtzyyg.2020074

Download: PDF(2410 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Cloud, snow and fog are important factors affecting the quality of optics remote sensing images, and hence researchers should detect the range of cloud, snow, fog in remote sensing images and remove unwanted images so as to improve the utilization of remote sensing images. In this paper, the authors studied the method based on Random Forest to detect cloud, snow, fog and tried to reduce the error detection rate by means of adding a “second detection”. Experiments show that this method has high detection accuracy and efficiency.

Keywords random forest classification detection of cloud, snow, fog second detection

ZTFLH:

P237

Issue Date: 18 March 2021

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yun XU
	Aiwen XU

Cite this article:

Yun XU,Aiwen XU. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020074 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/96

Fig.1 Cloud, snow, fog sample of NAD and MUX image

Fig.2 Algorithm flowchart of cloud, snow, fog classification

Fig.3 Land feature contrast with cloud, snow, fog

Fig.4 Cloud, snow, fog classification results of each satellite

Tab.1 Confusion matrix of the first detection results

Tab.2 Confusion matrix of the second detection results

Tab.3 Cloud, snow, fog detection accuracy of each satellite remote sensing images

[1]	Saunders R W, Kriebel K T. An improved method for detecting clear sky and cloudy radiances from AVHRR Data[J]. International Journal of Remote Sensing, 1987,9:123-150.
[2]	Ackerman S, Strabala K, Menzel W P, et al. Discriminating clear sky from clouds with MODIS[J]. Journal of Geophysical Research, 1998,103:141-157.
[3]	Merchant C J, Harris A R, Maturi E, et al. Probabilistic physically based cloud screening of satellite infraredimagery for operational sea surface temperature retrieval[J]. Quarterly Journal of the Royal Meteorological Society, 2005,131(611):2735-2755.
[4]	Baum B, Tovinkere V TitlowJ. et al. Automated cloud classification of global AVHRR data using a fuzzy logic approach[J]. Journal of Applied Meteorology, 1997,36:1519-1540.
[5]	Bendix J, Thies B, Nauss T, et al. A feasibility study of daytime fog and low stratus detection with TERRA/AQUA MODIS over land[J]. Appl Meteor, 2006,13(2):111-125
[6]	李亚春, 孙涵, 李湘阁, 等. 用GMS-5气象卫星资料遥感监测白天雾的研究[J]. 南京气象学院学报, 2001,24(3):121-129.
[6]	Li Y C, Sun H, Li X G, et al. Study on detection of daytime fog using GMS-5 weather satellite data[J]. Journal of Nanjing Institute of Meteorology, 2001,24(3):121-129.
[7]	郑小波, 康为民, 田鹏举. EOS/MODIS遥感在贵州积雪监测中的应用[J]. 贵州农业科学, 2005,33(3):60-62.
[7]	Zheng X B, Kang W M, Tian P J. Application of EOS/MODIS remote sensing to snow cover in Guizhou[J]. Guizhou Agricultural Sciences, 2005,33(3):60-62.
[8]	殷青军, 杨英莲, 徐维新. NOAA卫星资料云雪识别方法的研究[J]. 高原气象, 2002,21(5):526-528. url: http://gyqx.westgis.ac.cn/CN/abstract/abstract4790.shtml
[8]	Ying Q J, Yang Y L, Xu W X. Research on distinguishing between cloud and snow with NOAA images[J]. Plateau Meteorology, 2002,21(5):526-528. url: http://gyqx.westgis.ac.cn/CN/abstract/abstract4790.shtml
[9]	丁海燕, 马灵玲, 李子扬, 等. 基于分形维数的全色影像云雪自动识别方法[J]. 遥感技术与应用, 2013,28(1):52-57. url: http://www.rsta.ac.cn/CN/abstract/abstract2206.shtml
[9]	Ding H Y, Ma L L, Li Z Y, et al. Automatic identification of cloud and snow based on fractal dimension[J]. Remote Sensing Technology and Application, 2013,28(1):52-57. url: http://www.rsta.ac.cn/CN/abstract/abstract2206.shtml
[10]	刘湘航, 曹晓光, 郁文霞. 基于分形维数的云检测算法[C]. 遥感科技论坛暨中国遥感应用协会2006年年会,太原, 2006.
[10]	Liu X H, Cao X G, Yu W X. A cloud detection algorithm based on fractal dimension[C]. Remote Sensing Science and Technology Forum in 2006,Taiyuan, 2006.
[11]	雷震. 随机森林及其在遥感影像处理中应用研究[D]. 上海:上海交通大学, 2012.
[11]	Lei Z. Random forests and its application in remote sensing image processing[D]. Shanghai:Shanghai Jiaotong University, 2012.
[12]	Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32. doi: 10.1023/A:1010933404324 url: http://www.springerlink.com/content/u0p06167n6173512/
[13]	Breiman L. Bagging predictors[J]. Machine Learning, 1996,24(2):123-140.

[1]	WU Weichao, YE Fawang. Cloud detection of Sentinel-2 images for multiple backgrounds[J]. Remote Sensing for Natural Resources, 2023, 35(3): 124-133.
[2]	XI Lei, SHU Qingtai, SUN Yang, HUANG Jinjun, SONG Hanyue. Optimizing an ICESat2-based remote sensing estimation model for the leaf area index of mountain forests in southwestern China[J]. Remote Sensing for Natural Resources, 2023, 35(3): 160-169.
[3]	PARIHA Helili, ZAN Mei. Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions[J]. Remote Sensing for Natural Resources, 2023, 35(3): 201-211.
[4]	LIANG Jintao, CHEN Chao, ZHANG Zili, LIU Zhisong. A random forest-based method integrating indices and principal components for classifying remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(3): 35-42.
[5]	WU Yuxin, WANG Juanle, HAN Baomin, YAN Xinrong. Forest classification for Motuo County: A method based on spatio-temporal-spectral characteristics[J]. Remote Sensing for Natural Resources, 2023, 35(1): 180-188.
[6]	ZHANG Hao, GAO Xiaohong, SHI Feifei, LI Runxiang. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J]. Remote Sensing for Natural Resources, 2022, 34(4): 144-154.
[7]	DENG Jingwen, TIAN Yichao, ZHANG Qiang, TAO Jin, ZHANG Yali, HUANG Shengguang. Application of airborne LiDAR in the estimation of the mean height of mangrove stand[J]. Remote Sensing for Natural Resources, 2022, 34(3): 129-137.
[8]	WANG Chunxia, ZHANG Jun, LI Yixu, PHOUMILAY. The construction and verification of a water index in the complex environment based on GF-2 images[J]. Remote Sensing for Natural Resources, 2022, 34(3): 50-58.
[9]	WANG Xuejie, SHI Guoping, ZHOU Ziqin, ZHEN Yang. Revision of solar radiation product ERA5 based on random forest algorithm[J]. Remote Sensing for Natural Resources, 2022, 34(2): 105-111.
[10]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[11]	LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[12]	GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[13]	LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[14]	WU Qian, JIANG Qigang, SHI Pengfei, ZHANG Lili. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.
[15]	YANG Lijuan. Estimating PM_2.5 concentrations in eastern coastal area of China using a two-stage random forest model[J]. Remote Sensing for Land & Resources, 2020, 32(4): 137-144.

Viewed

Full text

Abstract

Cited

Shared

Discussed