深度卷积神经网络的遥感影像水体识别
Water identification from the GF-1 satellite image based on the deep convolutional neural networks
- 2022年26卷第11期 页码：2304-2316
纸质出版日期： 2022-11-07
DOI： 10.11834/jrs.20210175
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
从遥感影像中准确识别水体信息对水资源管理和洪涝灾害监测有重要意义。目前，传统遥感水体识别方法仍存在着不足，难以满足实际应用中的精度要求。近年来，卷积神经网络CNN（Convolutional Neural Network）的快速发展，为高分辨率遥感水体识别提供了新的思路。本文基于高分一号卫星数据，利用DenseNet、ResNet、VGG、HRNet等CNN模型和传统的归一化差异水体指数（NDWI）进行洪泽湖地区不同季节的水体识别，并采用精确度（
P
）、召回率（
R
）、
F
1分数和误判率（
MRate
）等指标来评价各种方法的水体识别能力。在DenseNet经典结构中加入了上采样过程和跳层连接结构，以解决梯度爆炸和梯度消失问题；采用OSTU方法确定NDWI的最优阈值，以降低水体识别的不确定性。得出主要结论如下：（1）所有CNN网络模型的水体识别效果都显著优于传统NDWI方法；例如，NDWI识别的精确度仅为0.779，而所有CNN网络模型的识别精确度均高于0.922。（2）改进后的DenseNet模型有效缓解了梯度爆炸与梯度消失的问题，其水体识别结果在识别精确度
P
（0.960）和误判率（0.041）等方面，明显优于其他CNN模型；同时，修改后的DenseNet模型训练时间更短，且损失函数最低，训练效率远优于其他CNN网络。（3）改进后的DenseNet模型对水体细部特征有很好的识别能力，能够准确识别不同季节中不同形状与颜色的水体。上述结果表明，CNN模型是从卫星影像中识别水体的可靠工具，而改进后DenseNet模型的识别效果尤其显著。
Abstract
Rapid and accurate identification of water bodies from remote sensing images is of great significance to water resources management and flood disaster monitoring. At present
traditional methods for identifying water bodies from satellite images still have shortcomings
and sometimes the results are not accurate enough to meet the practical needs. Recently
the Convolutional Neural Network (CNN) methods have emerged and been rapidly developed
providing a new idea for a identifying water bodies from satellite images. In this work
the Densely Connected Deep Convolutional Neural Network (DenseNet) is used to identify water bodies in the Hongze Lake area
together with the ResNet
VGG
HRNet networks
and the traditional method of Normalized Difference Water Index (NDWI). We have added the upsampling process and the skip connection structure to its classical structure to improve the performance of the DenseNet network. These methods are applied to the GF-1 satellite images of the Hongze Lake area to identify the water bodies in different seasons. Experiments are conducted to determine the optimal parameters of DenseNet
ResNet
VGG
and HRNet networks for water body identification. Moreover
the OSTU method is used to determine the optimal threshold of NDWI to reduce the uncertainty of threshold determination. Several indices of precision (P)
recall (R)
F1 score
and misclassification rate (MRate) are used to evaluate the performance of these methods. The main conclusions we have reached are as follows: (1) All the CNN models of ResNet
VGG
HRNet
and DenseNet have significantly outperformed the traditional NDWI method; for example
the precision (P) of water identification by using the NDWI method is only 0.779 compared with ground truth; however
it is highly improved to >0.922 by utilizing the CNN models. (2) The modified DenseNet model has effectively alleviated the problems of gradient explosion and disappearance
and the water body identification result is much better than the other CNN models
e.g.
with the best P (0.960) and MRate (0.041). The training efficiency of the modified DenseNet model also appears far better than that of the other CNN models with the shorted training time
and the lowest loss function. (3) The modified DenseNet model shows also a better capability in identifying the fine features of water bodies
even if their shapes and water colors change largely in different seasons. These results have indicated that the CNN models are good tools for identifying water bodies from satellite images
and the modified DenseNet model appears to be the most promising one among them.
关键词
卫星遥感水体识别归一化差异水体指数卷积神经网络
Keywords
satellite imageswater identificationnormalized difference water indexConvolutional Neural Network
references
Bengio Y. 2012. Practical recommendations for gradient-based training of deep architectures//Montavon G, Orr G B, Müller K R eds.Neural Networks: Tricks of the Trade. Berlin, Heidelberg: Springer, 437-478 [DOI: 10.1007/978-3-642-35289-8_26http://dx.doi.org/10.1007/978-3-642-35289-8_26]
Bi H Y, Wang S Y, Zeng J Y, Zhao Y, Wang H and Yin H. 2012. Comparison and analysis of several common water extraction methods based on TM image. Remote Sensing Information, 27(5): 77-82
毕海芸, 王思远, 曾江源, 赵岩, 王辉, 殷慧. 2012. 基于TM影像的几种常用水体提取方法的比较和分析. 遥感信息, 27(5): 77-82 [DOI: 10.3969/j.issn.1000-3177.2012.05.014http://dx.doi.org/10.3969/j.issn.1000-3177.2012.05.014]
Cortes C and Vapnik V. 1995. Support-vector networks. Machine Learning, 20(3): 273-297 [DOI: 10.1007/BF00994018http://dx.doi.org/10.1007/BF00994018]
Feyisa G L, Meilby H, Fensholt R and Proud S R. 2014. Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140(1): 23-35 [DOI: 10.1016/j.rse.2013.08.029http://dx.doi.org/10.1016/j.rse.2013.08.029]
Fu D and Yue J P. 2019. Hongze Lake water surface area based on Landsat image and its change analysis. Journal of Gansu Sciences, 31(2): 35-39
伏蝶, 岳建平. 2019. 基于Landsat影像的洪泽湖水面面积及其变化分析. 甘肃科学学报, 31(2): 35-39 [DOI: 10.16468/j.cnki.issn1004-0366.2019.02.006http://dx.doi.org/10.16468/j.cnki.issn1004-0366.2019.02.006]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778 [DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Hinton G E, Osindero S and Teh Y W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527-1554 [DOI: 10.1162/neco.2006.18.7.1527http://dx.doi.org/10.1162/neco.2006.18.7.1527]
Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional network//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 2261-2269 [DOI: 10.1109/CVPR.2017.243http://dx.doi.org/10.1109/CVPR.2017.243]
Kou D L, Quan J C and Zhang Z W. 2019. Research on progress of object detection framework based on deep learning. Computer Engineering and Applications, 55(11): 25-34
寇大磊, 权冀川, 张仲伟. 2019. 基于深度学习的目标检测框架进展研究. 计算机工程与应用, 55(11): 25-34 [DOI: 10.3778/j.issn.1002-8331.1902-0254http://dx.doi.org/10.3778/j.issn.1002-8331.1902-0254]
Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90 [DOI: 10.1145/3065386http://dx.doi.org/10.1145/3065386]
Liu W B, Wang Z D, Liu X H, Zeng N Y, Liu Y R and Alsaadi F E. 2017. A survey of deep neural network architectures and their applications. Neurocomputing, 234: 11-26 [DOI: 10.1016/j.neucom.2016.12.038http://dx.doi.org/10.1016/j.neucom.2016.12.038]
Lu H T and Zhang Q C. 2016. Applications of Deep Convolutional Neural Network in Computer Vision. Journal of Data Acquisition and Processing, 31(1): 1-17
卢宏涛, 张秦川. 2016. 深度卷积神经网络在计算机视觉中的应用研究综述.数据采集与处理, 31(1): 1-17 [DOI: 10.16337/j.1004-9037.2016.01.001http://dx.doi.org/10.16337/j.1004-9037.2016.01.001]
McFeeters S K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Mei H P, Wang Z L, Liu M, Zhou J M. 2021. Characteristic Water Levels of Hongze Lake in the Past Five Decades:Variation Rules and Influencing Factors. Journal of Yangtze River Scientific Research Institute, 38(1): 35-40
梅海鹏,王振龙,刘猛,周佳敏. 2021. 洪泽湖近50 a特征水位变化规律及影响因素.长江科学院院报, 38(1): 35-40 [DOI: 10.11988/ckyyb.20191272http://dx.doi.org/10.11988/ckyyb.20191272]
Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]
Qu J Y, Sun X and Gao X. 2016. Remote sensing image target recognition based on CNN. Foreign Electronic Measurement Technology, 35(8): 45-50
曲景影, 孙显, 高鑫. 2016. 基于CNN模型的高分辨率遥感图像目标识别. 国外电子测量技术, 35(8): 45-50 [DOI: 10.3969/j.issn.1002-8978.2016.08.011http://dx.doi.org/10.3969/j.issn.1002-8978.2016.08.011]
Saito S, Yamashita T and Aoki Y. 2016. Multiple object extraction from aerial imagery with convolutional neural networks. Electronic Imaging, 60(1): 1-9 [DOI: 10.2352/ISSN.2470-1173.2016.10.ROBVIS-392http://dx.doi.org/10.2352/ISSN.2470-1173.2016.10.ROBVIS-392]
Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition//Proceedings of the 3rd International Conference on Learning Representations. San Diego: ICLR: 1-14 [DOI: 10.48550/arXiv.1409.1556http://dx.doi.org/10.48550/arXiv.1409.1556]
Sheng Y W, Xiao Q G, Chen W Y. 1994. Application of FY-1B Meteorological Satellite Data to Monitor the Flood Disaster in Huaihe River Basin in Summer, 1991. Journal of Remote Sensing, 9(3): 228-233
盛永伟, 肖乾广, 陈维英. 1994. 应用FY－1B气象卫星监测1991年江淮洪水的研究. 环境遥感, 9(3): 228-233 [DOI: 10.11834/jrs.199403http://dx.doi.org/10.11834/jrs.199403]
Stehman S V. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1): 77-89 [DOI: 10.1016/S0034-4257(97)00083-7http://dx.doi.org/10.1016/S0034-4257(97)00083-7]
Wang G J, Wu M J, Wei X K and Song H H. 2020a. Water identification from high-resolution remote sensing images based on multidimensional densely connected convolutional neural networks. Remote Sensing, 12(5): 795 [DOI: 10.3390/rs12050795http://dx.doi.org/10.3390/rs12050795]
Wang J D, Sun K, Cheng T H, Jiang B R, Deng C R, Zhao Y, Liu D, Mu Y D, Tan M K, Wang X G, Liu W Y and Xiao B. 2020b. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10): 3349-3364 [DOI: 10.1109/TPAMI.2020.2983686http://dx.doi.org/10.1109/TPAMI.2020.2983686]
Wang X, Sui L C, Zhong M Q, Li D M and Dang L L. 2018. Fully convolution neural networks for water extraction of remote sensing images. Bulletin of Surveying and Mapping, (6): 41-45
王雪, 隋立春, 钟棉卿, 李顶萌, 党丽丽. 2018. 全卷积神经网络用于遥感影像水体提取. 测绘通报, (6): 41-45 [DOI: 10.13474/j.cnki.11-2246.2018.0173http://dx.doi.org/10.13474/j.cnki.11-2246.2018.0173]
Wang Z H. 1992. Application of remote sensing techniques to the study of geological disaster. Journal of Remote Sensing, 7(3): 190-195
王治华. 1992. 遥感技术在地质灾害调查、监测和防治中的应用. 环境遥感, 7(3): 190-195 [DOI: 10.11834/jrs.1992024http://dx.doi.org/10.11834/jrs.1992024]
Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5): 589-595
徐涵秋. 2005. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究. 遥感学报, 9(5): 589-595 [DOI: 10.11834/jrs.20050586http://dx.doi.org/10.11834/jrs.20050586]
Xu H Q. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033 [DOI: 10.1080/01431160600589179http://dx.doi.org/10.1080/01431160600589179]
Yang W L, Yang M H and Qi H X. 2012. Water body extracting from TM image based on BPNN. Science of Surveying and Mapping, 37(1): 148-150
杨文亮, 杨敏华, 祁洪霞. 2012. 利用BP神经网络提取TM影像水体. 测绘科学, 37(1): 148-150 [DOI: 10.16251/j.cnki.1009-2307.2012.01.011http://dx.doi.org/10.16251/j.cnki.1009-2307.2012.01.011]
Yin B C, Wang W T and Wang L C. 2015. A review of deep learning. Journal of Beijing University of Technology, 41(1): 48-59
尹宝才, 王文通, 王立春. 2015. 深度学习研究综述. 北京工业大学学报, 41(1): 48-59 [DOI: 10.11936/bjutxb2014100026http://dx.doi.org/10.11936/bjutxb2014100026]
Yin Y Q, Li J G, Yu T, Yang H Y and Zhang Y H. 2015. The Study of Object-oriented Water Body Extraction Method Based on High Resolution RS Image. Bulletin of Surveying and Mapping, (1): 81-85
殷亚秋, 李家国, 余涛, 杨红艳, 张永红. 2015. 基于高分辨率遥感影像的面向对象水体提取方法研究. 测绘通报, (1): 81-85 [DOI: 10.13474/j.cnki.11-2246.2015.0016http://dx.doi.org/10.13474/j.cnki.11-2246.2015.0016]
Yue Y, Gong J and Wang D. 2010. The extraction of water information based on SPOT5 image using object-oriented method//2010 18th International Conference on Geoinformatics. Beijing: IEEE: 1-5 [DOI: 10.1109/GEOINFORMATICS.2010.5567695http://dx.doi.org/10.1109/GEOINFORMATICS.2010.5567695]
Zhang B, Li J S, Shen Q, Wu Y H, Zhang F F, Wang S L, Yao Y, Guo L N and Yin Z Y. 2021. Recent research progress on long time series and large scale optical remote sensing of inland water. National Remote Sensing Bulletin, 25(1): 37-52
张兵, 李俊生, 申茜, 吴艳红, 张方方, 王胜蕾, 姚月, 郭立男, 殷子瑶. 2021. 长时序大范围内陆水体光学遥感研究进展. 遥感学报, 25(1): 37-52 [DOI: 10.11834/jrs.20210570http://dx.doi.org/10.11834/jrs.20210570]
Zheng Y P, Li G Y and Li Y. 2019. Survey of application of deep learning in image recognition. Computer Engineering and Applications, 55(12): 20-36
郑远攀, 李广阳, 李晔. 2019. 深度学习在图像识别中的应用研究综述. 计算机工程与应用, 55(12): 20-36 [DOI: 10.3778/j.issn.1002-8331.1903-0031http://dx.doi.org/10.3778/j.issn.1002-8331.1903-0031]
Zhou F Y, Jin L P and Dong J. 2017. Review of convolutional neural network. Chinese Journal of Computers, 40(6): 1229-1251
周飞燕, 金林鹏, 董军. 2017. 卷积神经网络研究综述. 计算机学报, 40(6): 1229-1251 [DOI: 10.11897/SP.J.1016.2017.01229http://dx.doi.org/10.11897/SP.J.1016.2017.01229]
Zhu C M, Luo J C, Shen Z F and Li J L. 2013. River linear water adaptive auto-extraction on remote sensing image aided by DEM. Acta Geodaetica et Cartographica Sinica, 42(2): 277-283
朱长明, 骆剑承, 沈占锋, 李均力. 2013. DEM辅助下的河道细小线性水体自适应迭代提取. 测绘学报, 42(2): 277-283 [DOI: CNKI:SUN:CHXB.0.2013-02-019http://dx.doi.org/CNKI:SUN:CHXB.0.2013-02-019]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
武汉大学 测绘遥感信息工程国家重点实验室
北京师范大学 遥感科学国家重点实验室
中国地质大学(武汉) 地球物理与空间信息学院
河北工业大学 电子与信息工程学院
自然资源部第二海洋研究所 卫星海洋环境动力学国家重点实验室
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部