结合超像素和卷积神经网络的国产高分辨率遥感影像云检测方法

doi:10.13474/j.cnki.11-2246.2019.0010

测绘通报 ›› 2019, Vol. 0 ›› Issue (1): 50-55.doi: 10.13474/j.cnki.11-2246.2019.0010

结合超像素和卷积神经网络的国产高分辨率遥感影像云检测方法

徐启恒¹, 黄滢冰¹, 陈洋^2,3

1. 东莞市测绘院, 广东东莞 523129;
2. 辽宁工程技术大学测绘与地理科学学院, 辽宁阜新 123000;
3. 中国测绘科学研究院, 北京 100830

收稿日期:2018-04-02 出版日期:2019-01-25 发布日期:2019-02-14
通讯作者: 陈洋。E-mail:chenyang1017@126.com E-mail:chenyang1017@126.com
作者简介:徐启恒(1986-),男,硕士,高级工程师,主要从事数字城市、GIS与城乡规划、空间数据挖掘等工作。E-mail:xuqiheng@163.com
基金资助:
国家自然科学基金重点项目（41431178）；高等学校博士点专项科研基金（20120171110030）

Cloud detection for Chinese high resolution remote sensing imagery using combining superpixel with convolution neural network

XU Qiheng¹, HUANG Yingbing¹, CHEN Yang^2,3

1. Dongguan Institute of Surveying and Mapping, Dongguan 523129, China;
2. School of Geomatics, Liaoning Technology University, Fuxin 123000, China;
3. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received:2018-04-02 Online:2019-01-25 Published:2019-02-14

摘要/Abstract

摘要： 由于国产高分辨率卫星遥感影像波段少、光谱范围窄，导致传统云检测方法精度低。本文提出了基于卷积神经网络的高分辨率遥感影像云检测方法。首先采用主成分分析非监督预训练网络结构，获取待测遥感影像云特征；然后采用超像素分割方法进行影像分割；最后将检测结果影像块拼接，完成整幅影像云检测。试验效果评价表明，基于卷积神经网络的高分辨率遥感影像云检测方法不受光谱范围限制，云检测精度高，误判较少，适合国产高分辨遥感影像云检测。

关键词: 高分辨率遥感影像, 卷积神经网络, 云检测, 超像素分割

Abstract: Accurate cloud detection in Chinese high resolution remote sensing imagery is very low, due to the fact that there are few image bands and limited spectral range. In this study, high resolution remote sensing image cloud detection method based on convolution neural network is proposed. At first, in terms of network training, this paper obtains the feature of remote sensing images through using principal component transform and unsupervised pre-training network structure. Then it enters the image block into the network for cloud detection using the superpixel segmentation method for segmentation. Finally, the test image block is spliced, and the entire image cloud detection is completed. Experiments show that this method has high computational accuracy and the ranges of the spectral bands are not a limit.

Key words: high resolution remote sensing image, convolution neural network, cloud detection, superpixel segmentation

中图分类号:

P237

徐启恒, 黄滢冰, 陈洋. 结合超像素和卷积神经网络的国产高分辨率遥感影像云检测方法[J]. 测绘通报, 2019, 0(1): 50-55.

XU Qiheng, HUANG Yingbing, CHEN Yang. Cloud detection for Chinese high resolution remote sensing imagery using combining superpixel with convolution neural network[J]. 测绘通报, 2019, 0(1): 50-55.

参考文献

[1] 陈洋,范荣双,徐启恒,等.结合光谱相似和相位一致的高分辨率影像分类[J].测绘科学,2018,43(11):1-7.
[2] ZHU Z, WOODCOCK C E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data:an algorithm designed specifically for monitoring land cover change[J]. Remote Sensing of Environment, 2014, 152:217-234. DOI:10.1016/j.rse.2014.06.012.
[3] 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.
[4] LI Z, SHEN H, LI H, et al. Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery[J]. Remote Sensing of Environment, 2017, 191:342-358.DOI:10.1016/j.rse.2017.01.026.
[5] TAN K, ZHANG Y, TONG X. Cloud extraction from Chinese high resolution satellite imagery by probabilistic latent semantic analysis and object-based machine learning[J]. Remote Sensing, 2016, 8(11):963.
[6] 谭凯,张永军,童心,等.国产高分辨率遥感卫星影像自动云检测[J].测绘学报,2016,45(5):581-591.
[7] 高贤君,万幼川,郑顺义,等.航空摄影过程中云的实时自动检测[J].光谱学与光谱分析,2014,34(7):1909-1913.
[8] AGHDAM H H, HERAVI E J. Convolutional neural networks[M].[S.l.]:Springer International Publishing, 2017:85-130.
[9] 郁文霞,曹晓光,徐琳,等.遥感图像云自动检测[J].仪器仪表学报,2006(S3):2184-2186.
[10] 陈振炜,张过,宁津生,等.资源三号测绘卫星自动云检测[J].测绘学报,2015,44(3):292-300.
[11] CIRESAN D C, MEIER U, GAMBARDELLA L M, et al. Convolutional neural network committees for handwritten character classification[C]//Proceedings of 2011 International Conference on Document Analysis and Recognition (ICDAR).[S.l.]:IEEE, 2011:1135-1139.
[12] CHEN Y, FAN R, BILAL M, et al. Multilevel cloud detection for high-resolution remote sensing imagery using multiple convolutional neural networks[J]. ISPRS International Journal of Geo-Information, 2018, 7(5):181.
[13] 陈洋,范荣双,王竞雪,等.结合最小噪声分离变换和卷积神经网络的高分辨影像分类方法[J].激光与光电子学进展,2017,54(10):440-445.
[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] CHEN Y, FAN R, YANG X, et al. Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning[J]. Water, 2018, 10(5):585.
[16] 艾玲梅,叶雪娜.基于循环卷积神经网络的目标检测与分类[J].计算机技术与发展,2018,28(2):31-35.
[17] SARANATHAN A M, PARENTE M. Uniformity-based superpixel segmentation of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3):1419-1430.
[18] 汪云飞,冯国强,刘华伟,等.基于超像素的均值-均方差暗通道单幅图像去雾方法[J].自动化学报,2018,44(3):481-489.
[19] ROTH H R, LU L. Improving computer-aided detection using convolutional neural networks and random view aggregation[J]. IEEE Transactions on Medical Imaging, 2016, 35(5):1170-1181.
[20] 杨艳,许道云.优化加权核K-means聚类初始中心点的SLIC算法[J].计算机科学与探索,2018,12(3):494-501.
[21] 陈洋,范荣双,王竟雪,等.结合相位一致和分水岭变换的高分辨率影像分割方法[J].激光与光电子学进展, 2017, 54(9):092803.

结合超像素和卷积神经网络的国产高分辨率遥感影像云检测方法

Cloud detection for Chinese high resolution remote sensing imagery using combining superpixel with convolution neural network

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