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基于深度学习的资源三号卫星遥感影像云检测方法

Cloud Detection of ZY-3 Satellite Remote Sensing Images Based on Deep Learning

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摘要

针对资源三号卫星影像波段少、光谱范围受限的特点, 提出了基于深度学习的资源三号卫星遥感影像的云检测方法。首先, 采用主成分分析非监督预训练网络结构, 获得了待测遥感影像特征; 其次, 为减少在池化过程中影像特征信息的丢失, 提出自适应池化模型, 该模型能很好地挖掘影像特征信息; 最后, 将影像特征输入支持向量机分类器进行分类, 获得了云检测结果。选取典型区域进行云检测实验, 并与传统Otsu方法进行对比。结果表明: 所提方法的检测精度高, 且不受光谱范围的限制, 可用于资源三号卫星多光谱影像和全色影像的云检测。

Abstract

The cloud detection method of ZY-3 satellite remote sensing images based on deep learning is proposed to solve the problem of the images with few image bands and limited spectral range. Firstly, we obtain the feature of remote sensing images measured with the unsupervised pre-training network structure of principal component analysis. Secondly, we put forward the adaptive pooling model, which can well mine images in order to reduce the loss of image feature information in the pooling process. Finally, the image features are input into the support vector machine classifier to obtain the cloud detection results. The typical regions are selected for cloud detection experiments, and the detection results are compared with that of the traditional Otsu method. The results show that the proposed method has high detection precision and is not limited by the spectral range, and it can be used for the multi-spectral and panchromatic images cloud detection of ZY-3 satellite.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:P237

DOI:10.3788/aos201838.0128005

所属栏目:遥感与传感器

基金项目:国家重点研发计划项目(2016YFC0803100)、国家自然科学基金(41101452)、高等学校博士学科点专项科研基金(20112121120003)

收稿日期:2017-08-15

修改稿日期:2017-09-24

网络出版日期:--

作者单位    点击查看

陈洋:辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新123000
范荣双:国家测绘工程技术研究中心, 北京100039
王竞雪:辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新123000
陆婉芸:南京大学地理与海洋科学学院, 江苏 南京210023
朱红:国家测绘地理信息局卫星测绘应用中心, 北京 100048
楚清源:国家测绘工程技术研究中心, 北京100039

联系人作者:陈洋(874153187@qq.com)

备注:陈洋(1991-), 男, 硕士研究生, 主要从事影像分割、地物信息智能提取和深度学习方面的研究。E-mail: 874153187@qq.com

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引用该论文

Chen Yang,Fan Rongshuang,Wang Jingxue,Lu Wanyun,Zhu Hong,Chu Qingyuan. Cloud Detection of ZY-3 Satellite Remote Sensing Images Based on Deep Learning[J]. Acta Optica Sinica, 2018, 38(1): 0128005

陈洋,范荣双,王竞雪,陆婉芸,朱红,楚清源. 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): 0128005

被引情况

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【3】闫苗,赵红东,李宇海,张洁,赵泽通. 基于卷积神经网络的高光谱遥感地物多分类识别. 激光与光电子学进展, 2019, 56(2): 21702--1

【4】刘心燕,孙林,杨以坤,周雪莹,王权,陈婷婷. 高分四号卫星数据云和云阴影检测算法. 光学学报, 2019, 39(1): 128001--1

【5】高琳,宋伟东,谭海,刘阳. 多尺度膨胀卷积神经网络资源三号卫星影像云识别. 光学学报, 2019, 39(1): 104002--1

【6】裴亮,刘阳,谭海,高琳. 基于改进的全卷积神经网络的资源三号遥感影像云检测. 激光与光电子学进展, 2019, 56(5): 52801--1

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【8】徐冬宇,厉小润,赵辽英,舒锐,唐琪佳. 基于光谱分析和动态分形维数的高光谱遥感图像云检测. 激光与光电子学进展, 2019, 56(10): 101003--1

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【11】李鹏,张炎. 基于高斯混合模型和卷积神经网络的视频烟雾检测. 激光与光电子学进展, 2019, 56(21): 211502--1

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