Classification and detection of cloud, snow and fog in remote sensing images based on random forest
XU Yun1(), XU Aiwen2
1. Hangzhou Transportation Planning and Design Institute, Hangzhou 310003, China 2. Zhejiang Academy of Land and Space Planning, Hangzhou 310012, China
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.
许赟, 许艾文. 基于随机森林的遥感影像云雪雾分类检测[J]. 国土资源遥感, 2021, 33(1): 96-101.
XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
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
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.
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.
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.
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
[13]
Breiman L. Bagging predictors[J]. Machine Learning, 1996,24(2):123-140.