Cloud detection technology based on Gaussian mixture model for 
high-resolution remote sensing imagery

doi:10.3969/j.issn.1000-2006.201708009

Cloud detection technology based on Gaussian mixture model for high-resolution remote sensing imagery

YANG Fan, ZHAO Zengpeng, ZHANG Lei

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2018, Vol. 42 ›› Issue (04) : 134-140.

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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2018, Vol. 42 ›› Issue (04) : 134-140. DOI: 10.3969/j.issn.1000-2006.201708009

Cloud detection technology based on Gaussian mixture model for high-resolution remote sensing imagery

YANG Fan, ZHAO Zengpeng^*, ZHANG Lei

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Abstract

Abstract: 【Objective】 Developing cloud detection technology for high-resolution remote-sensing images is difficult, especially for thin edge and scattered clouds. We used morphological operations and polygon simplification techniques to accurately extract cloud-containing, high-resolution remote-sensing images.【Method】First, the Gaussian low-pass filter was used to smooth the image and create uniform shading. The image was then divided into three categories: cloudy, partly cloudy and cloudless. The Otsu threshold method was used on cloudy images, and Gaussian mixture model segmentation was used on partly cloudy images. Finally, the cloud area was morphologically processed to determine the final cloud area. 【Result】The high-resolution remote-sensing image cloud-detection algorithm based on Otsu threshold segmentation and Gaussian mixture model has good visual effect and can effectively improve the accuracy of cloud detection. The accuracy rate is 98.60%, recall rate of the method is approximately 90%, and the error rate is approximately 2.58%. It can accurately detect thick, thin and scattered clouds, and can also effectively reduce the misidentification of houses, roads and bare land. 【Conclusion】The high-resolution remote-sensing image cloud-detection algorithm based on Otsu threshold segmentation and Gaussian mixture model is moderately complex, and has a small computation size, fast operation speed, high-precision detection and wide applicability.

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YANG Fan, ZHAO Zengpeng, ZHANG Lei. Cloud detection technology based on Gaussian mixture model for high-resolution remote sensing imagery[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2018, 42(04): 134-140 https://doi.org/10.3969/j.issn.1000-2006.201708009

References

[1] 潘红播,张过,唐新明,等.资源三号测绘卫星传感器校正产品几何模型[J].测绘学报,2013,42(4):516-522. PAN H B, ZHANG G, TANG X M, et al. The geometrical model of sensor corrected products for ZY-3 Satellite[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(4):516-522.
[2] 陈荣元,郑晨,王雷光,等. MRF框架下的区域增长模型在城镇识别中的应用[J]. 测绘学报,2011,40(2):163-168. CHEN R Y, ZHEGN C, WANG L G, et al. A region growing model under the framework of MRF for urban detection[J]. Acta Geodaetica et Cartographica Sinica, 2011,40(2):163-168.
[3] CHEN G, DONG C E, WANG L G, et al. Support vector machines for cloud detection over ice-snow areas[J].Geo-spatial Information Science,2007,10(2):117-120.
[4] 梁栋,孔颉,胡根生,等.基于支持向量机的遥感影像厚云及云阴影去除[J].测绘学报,2012,41(2):225-231,238. LIANG D, KONG J, HU G S, et al. The Removal of thick cloud and cloud shadow of remote sensing image based on support vector machine[J]. Acta Geodaetica et Cartographica Sinica, 2012,41(2):225-231,238.
[5] 陈奋,闫冬梅,赵忠明.基于无抽样小波的遥感影像薄云监测与去除[J].武汉大学学报(信息科学版),2007,32(1):71-74. CHEN F, YAN D M, ZHAO Z M. Haze detection and removal in remote sensing images based on undecimated wavelet transform[J].Geomatics and Information Science of Wuhan University, 2007,32(1):71-74.
[6] 刘海波,沈晶,岳振勋,等.Visual C++数字图像处理技术详解[M].北京:机械工业出版社, 2014.
[7] 姒绍辉,胡伏原,顾亚军,等. 一种基于不规则区域的高斯滤波去噪算法[J]. 计算机科学,2014,41(11):313-316. DOI: 10.11896/j.issn.1002-137X.2014.11.062. SI S H, HU F Y, GU Y J, et al. Improved denoising algorithm based on non-regular area Gaussian filtering[J]. Computer Science, 2014,41(11):313-316.
[8] 陈万海,赵春晖. 基于高斯低通滤波的超光谱遥感图像分类研究[J]. 黑龙江大学自然科学学报,2007,24(6):751-756. DOI: 10.13482/j.issn1001-7011.2007.06.032. CHEN W H, ZHAO C H. Hyperspectral remote sensing classification based on Gaussian low pass filter[J]. Journal of Natural Science of Hei Long Jiang University, 2007,24(6):751-756.
[9] XIAN J P, ZHENG X W, SUN L, et al. Image segmentation based on 2D OTSU and simplified swarm optimization[R]. 2016 International Conference on Machine Learning and Cybernetics(ICMLC), 2016.
[10] 邓小炼,王星,艾思敏,等. 奇异值分解和阈值分割的遥感自适应变化检测[J]. 测绘科学,2016,41(8):38-42. DOI: 10.16251/J.CNKI.1009-2307.2016.08.008. DENG X L, WANG X, AI S M, et al. Adaptive change detective algorithm of remote sensing images based on SVD and OTSU[J]. Science of Surveying and Mapping, 2016,41(8):38-42.
[11] 许凯,秦昆,刘修国,等. 高斯混合模型云变换算法及其在图像分割中的应用[J]. 武汉大学学报(信息科学版),2013,38(10):1163-1166,1183. XU K, QIN K, LIU X G, et al. Cloud transformation method based on Gaussian mixed model and its application to image segmentation[J]. Geomatics and Information Science of Wuhan University, 2013,38(10):1163-1166,1183.
[12] RAFAEL C G, RICHARD E W, STEVEN L E. Digital image processing using MATLAB[M].Beijing:Publishing House of Electronics Industry,2013.
[13] 禹铭月,王卫安. 多边形形状简化及其质量评价[J]. 测绘与空间地理信息,2011,34(6):152-155. YU M Y, WANG W A. Polygonsimplification and quality evaluation[J]. Geomatics & Spatial Information Technology, 2011,34(6):152-155.

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