宋业冲,李英成,耿中元,丁晓波,裴亚健

• 1:中国测绘科学研究院
• 2:自然资源部航空遥感技术重点实验室
• 3:北京市低空遥感数据处理工程技术研究中心

摘要(Abstract)：

针对传统方法提取新增光伏用地精度低的问题,该文提出了一种基于集成学习的U-Net双网络变化信息融合的深度学习方法用于新增光伏用地的提取。首先对U-Net网络进行改进得到性能较好的两个变化检测网络模型,然后分别训练两个网络模型用于在高分辨率卫星影像上检测新增光伏用地,将训练好的两个网络模型的分类图融合再经过后处理得到最终的变化检测结果。通过实验表明:该方法明显优于传统变化检测方法,也提高了单网络模型变化检测结果的精度。

关键词(KeyWords)： 深度学习;U-Net网络;新增光伏用地提取;集成学习

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划子课题项目(2016YFC0803109,2016YFC0803104)

作者(Author): 宋业冲,李英成,耿中元,丁晓波,裴亚健

DOI: 10.16251/j.cnki.1009-2307.2020.11.013

参考文献(References)：

• [1] 眭海刚,冯文卿,李文卓,等.多时相遥感影像变化检测方法综述[J].武汉大学学报(信息科学版),2018,43(12):132-145.(SUI Haigang,FENG Wenqing,LI Wenzhuo,et al.Review of change detection methods for multi-temporal remote sensing imagery[J].Geomatics and Information Science of Wuhan University,2018,43(12):132-145.)
• [2] 李彦甫,刘勇.基于对象影像分析方法支持下的多源遥感影像光伏电场提取[J].测绘与空间地理信息,2016,39(3):68-72.(LI Yanfu,LIU Yong.Extraction of photovoltaic electric farms from multi-source remote sensing images based on object-based image analysis[J].Geomatics & Spatial Information Technology,2016,39(3):68-72.)
• [3] 王胜利,张连蓬,朱寿红,等.多共性特征联合的Landsat 8 OLI 遥感影像光伏电站提取[J].测绘通报,2018(11):46-52.(WANG Shengli,ZHANG Lianpeng,ZHU Shouhong,et al.Multi-invariant feature combined photovoltaic power plants extraction using multi-temporal Landsat 8 OLI imagery[J].Bulletin of Surveying and Mapping,2018(11):46-52.)
• [4] GUO Jiayi,PAN Zongxu,LEI Bin,et al.Automatic color correction for multisource remote sensing images with Wasserstein CNN[J].Remote Sensing,2017,9(5):483.
• [5] YAO Yuan,JIANG Zhiguo,ZHANG Haopeng,et al.Chimney and condensing tower detection based on faster R-CNN in high resolution remote sensing images[C]//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium.Fort Worth,TX:IEEE,2017:3329-3332.
• [6] 顾炼,许诗起,竺乐庆.基于FlowS-Unet的遥感图像建筑物变化检测[J].自动化学报,2018,28(1):15-20.(GU Lian,XU Shiqi,ZHU Leqing.Change detection for buildings in remote sensing images based on FlowS-Unet[J].Acta Automatica Sinica,2018,28(1):15-20.)
• [7] DOSOVITSKIY A,FISCHERY P,ILG E,et al.Flownet:learning optical flow with convolutional networks[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision(ICCV).Santiago,Chile:IEEE,2015:2758-2766.
• [8] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of 18 th International Conference on Medical Image Computing and Computer-Assisted Intervention.Munich,Germany:Springer,2015:234-241.
• [9] 王玉龙,蒲军,赵江华,等.基于生成对抗网络的地面新增建筑检测[J].计算机应用,2019,39(5):1518-1522.(WANG Yulong,PU Jun,ZHAO Jianghua,et al.Detection of new ground buildings based on generative adversarial network[J].Journal of Computer Applications,2019,39(5):1518-1522.)
• [10] 季顺平,魏世清.遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J].测绘学报,2019,48(4):448-459.(JI Shunping,WEI Shiqing.Building extraction via convolutional neural networks from an open remote sensing building dateset[J].ActaGeodaetica et Cartographica Sinica,2019,48(4):448-459.)
• [11] 苏健民,杨岚心,景维鹏.基于U-Net的高分辨率遥感图像语义分割方法[J].计算机工程与应用,2019,55(7):207-213.(SU Jianmin,YANG Lanxin,JING Weipeng.U-Net based semantic segmentation method for high resolution remote sensing image[J].Computer Engineering and Applications,2019,55(7):207-213.)
• [12] 伍广明,陈奇,SHIBASAKI R,等.基于U型卷积神经网络的航空影像建筑物检测[J].测绘学报,2018,47(6):864-872.(WU Guangming,CHEN Qi,SHIBASAKI R,et al.High precision building detection from aerial imagery using a U-Net like convolutional architecture[J].ActaGeodaetica et Cartographica Sinica,2018,47(6):864-872.)
• [13] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015:3431-3440.
• [14] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-05-01].https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2336495.
• [15] KINGMA D P,BA J.Adam:a method for stochastic optimization[J].Computer Science,2014:1412:6980.
• [16] DROZDZAL M,VORONTSOV E,CHARTRAND G,et al.The importance of skip connections in biomedical image segmentation[C]//International Workshop on LargeScale Annotation of Biomedical Data and Expert Label Synthesis.[S.1.]:Springer,2016:179-187.