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结合全卷积神经网络与条件随机场的资源3号遥感影像云检测

Cloud Detectionof ZY-3 Remote Sensing Images Based on Fully Convolutional Neural Network and Conditional Random Field

裴亮   刘阳   高琳  
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摘要

提出一种结合全卷积神经网络与条件随机场的资源3号卫星遥感影像云检测方法。优化了全卷积神经网络(FCN)模型,对3次上采样后的全卷积神经网络(FCN-8s)进行上采样,采用自适应+动量算法调整参数学习率加速收敛;将全卷积神经网络与条件随机场结合,以全卷积输出影像作为前端一阶势,高斯核函数作为后端二阶势;加入mean-shift区域约束0条件保护影像的局部特征信息,运用平均场算法推断条件随机场模型后验概率。实验结果表明,本研究提出的云检测方法可将影像云区识别准确率提高至97.38%,较FCN-8s算法提高13.42%。

Abstract

A novel method for the cloud detection of ZY-3 satellite remote sensing images is proposed based on the fully convolutional neural network (FCN) combined with the conditional random field. The model of a fully convolutional neural network is optimized and the FCN after three times of upsampling (FCN-8s) is upsampled. The momentum combined adaptive algorithm is used for the acceleration of convergence by adjusting the learning rate of parameters. The fully convolutional neural network is combined with the conditional random field, the fully convolutional output image is taken as the first-order potential of the front end, and the Gaussian kernel function is used as the second-order potential of the back end. The mean-shift regional constraints are added to protect the local feature information of images and the posterior probability of the conditional random field model is inferred by the mean field algorithm. The experimental results show that the proposed cloud detection method can increase the identification accuracy rate of an image cloud region to 97.38%, which is 13.42% higher than that from FCN-8s.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:P237

DOI:10.3788/lop56.102802

所属栏目:遥感与传感器

基金项目:辽宁省教育厅科学研究项目(L2015215)

收稿日期:2018-09-25

修改稿日期:2018-09-30

网络出版日期:2018-11-22

作者单位    点击查看

裴亮:辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新 123000
刘阳:辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新 123000
高琳:辽宁工程技术大学测绘与地理科学学院, 辽宁 阜新 123000

联系人作者:刘阳(764039378@qq.com)

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引用该论文

Pei Liang,Liu Yang,Gao Lin. Cloud Detectionof ZY-3 Remote Sensing Images Based on Fully Convolutional Neural Network and Conditional Random Field[J]. Laser & Optoelectronics Progress, 2019, 56(10): 102802

裴亮,刘阳,高琳. 结合全卷积神经网络与条件随机场的资源3号遥感影像云检测[J]. 激光与光电子学进展, 2019, 56(10): 102802

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