基于深度学习的高分遥感影像水体提取模型研究
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摘要
从高分辨率卫星遥感影像中提取水体对于水体监测和管理具有重要意义,而阴影和建筑物的干扰制约了水体提取的精度。该文分别利用卷积神经网络和Deeplabv3语义分割神经网络,开展了高分辨率卫星遥感数据水体提取研究,探讨深度学习在水体提取中的应用能力。首先,以高分辨率卫星遥感影像为数据源,分别建立水体分类数据集和水体语义分割数据集,构建并训练卷积神经网络及Deeplabv3网络,得到最优的两种水体提取模型,进一步利用同一测试集对两种模型和其他方法进行精度评价。结果表明,卷积神经网络、Deeplabv3方法精度分别达到95.09%和92.14%,均高于水体指数法、面向对象法和支持向量机法;而且该两种深度学习方法都能够有效去除阴影和建筑物的影响,说明了深度学习方法的有效性,其中,卷积神经网络的适用性更好。
Extracting water body from high resolution satellite remote sensing images is of great significance for its monitoring and management.Because of the interference of shadows and buildings,the accuracy of water body extraction is limited.In this paper,the convolution neural network and Deeplabv3 semantic segmentation neural network are used to carry out the research of water body extraction from high resolution satellite remote sensing data,and the applicability of deep learning in water body extraction is discussed.Taking high-resolution satellite remote sensing images as the data source,data sets for water classification and water body semantic segmentation were established,convolution neural network and Deeplabv3 network were then constructed and trained,and two optimal water body extraction models were obtained.The accuracy of the two models and other methods was further evaluated using the same test set.The results show that the accuracy of the convolution neural network method is 95.09%,and that of the Deeplabv3 method is 92.14%.The convolution neural network has better applicability.The precision of two deep learning methods is higher than that of the water body index method,the object-oriented method and the support vector machine method.Moreover,these two deep learning methods have demonstrated their effectiveness in removing the influence of shadows and buildings.
Extracting water body from high resolution satellite remote sensing images is of great significance for its monitoring and management.Because of the interference of shadows and buildings,the accuracy of water body extraction is limited.In this paper,the convolution neural network and Deeplabv3 semantic segmentation neural network are used to carry out the research of water body extraction from high resolution satellite remote sensing data,and the applicability of deep learning in water body extraction is discussed.Taking high-resolution satellite remote sensing images as the data source,data sets for water classification and water body semantic segmentation were established,convolution neural network and Deeplabv3 network were then constructed and trained,and two optimal water body extraction models were obtained.The accuracy of the two models and other methods was further evaluated using the same test set.The results show that the accuracy of the convolution neural network method is 95.09%,and that of the Deeplabv3 method is 92.14%.The convolution neural network has better applicability.The precision of two deep learning methods is higher than that of the water body index method,the object-oriented method and the support vector machine method.Moreover,these two deep learning methods have demonstrated their effectiveness in removing the influence of shadows and buildings.
引文
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[2] MCFEETERS S K.The use of the normalized difference water index (NDWI) in the delineation of open water features[J].Journal of Remote Sensing,1996,17(7):1425-1432.
[3] 段秋亚,孟令奎,樊志伟,等.GF-1卫星影像水体信息提取方法的适用性研究[J].国土资源遥感,2015,27(4):79-84.
[4] 殷亚秋,李家国,余涛,等.基于高分辨率遥感影像的面向对象水体提取方法研究[J].测绘通报,2015,61(1):81-85.
[5] 陈静波,刘顺喜,汪承义,等.基于知识决策树的城市水体提取方法研究[J].遥感信息,2013,28(1):29-33.
[6] 吴际通,谭伟,喻理飞.基于TM/ETM+影像的不同水体指数对比研究[J].测绘科学,2013,38(4):193-195.
[7] 徐涵秋.利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J].遥感学报,2005,9(5):589-595.
[8] 闫霈,张友静,张元.利用增强型水体指数(EWI)和GIS去噪音技术提取半干旱地区水系信息的研究[J].遥感信息,2007,22(6):62-67.
[9] 范登科,李明,贺少帅.基于环境小卫星CCD影像的水体提取指数法比较[J].地理与地理信息科学,2012,28(2):14-19.
[10] 王航,秦奋.遥感影像水体提取研究综述[J].测绘科学,2018,43(5):26-35.
[11] HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[12] KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNet Classification with Deep Convolutional Neural Networks[C].NIPS.Curran Associates Inc,2012.
[13] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].2015.3431-3440.
[14] 何海清,杜敬,陈婷,等.结合水体指数与卷积神经网络的遥感水体提取[J].遥感信息,2017,32(5):86-90.
[15] 王雪,隋立春,钟棉卿,等.全卷积神经网络用于遥感影像水体提取[J].测绘通报,2018,64(6):44-48.
[16] 陈前,吴俣,叶菁菁,等.面向城市区域的遥感影像云检测方法[J].遥感信息,2018,33(5):57-61.
[17] 刘莹,孟庆岩,王永吉,等.基于特征优选与支持向量机的不透水面覆盖度估算方法[J].地理与地理信息科学,2018,34(1):24-31.
[18] NEBAUER C.Evaluation of convolutional neural networks for visual recognition[J].IEEE Trans Neural Netw,1998,9(4):685-696.
[19] CHENG G,HAN J W,LU X Q.Remote sensing image scene classification:Benchmark and state of the art[J].Proceedings of the IEEE,2017,105(10):1865-1883.
[20] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[A].ICLR 2015[C].2015.
[21] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected CRFs[J].Computer Science,2014(4):357-361.
[22] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Deeplab:Semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2018,40(4):834-848.
[23] CHEN L C,PAPANDREOU G,SCHROFF F,et al.Rethinking atrous convolution for semantic image segmentation[B/OL].https://arxiv.org/pdf/1706.05587.pdf,2017-12-31.
[24] CHEN L C,ZHU Y,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[A].Proceedings of the European Conference on Computer Vision (ECCV)[C].2018.801-818.
[25] HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(9):1904-1916.
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