计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 376-381.doi: 10.11896/jsjkx.210300260
王鑫1, 张昊宇2, 凌诚1
WANG Xin1, ZHANG Hao-yu2, LING Cheng1
摘要: 多光谱图像的分割是遥感图像解译的重要基础环节,SAR遥感图像中包含着复杂的地物目标信息,传统的分割方法存在耗时长、效率低等问题,导致传统图像分割方法的应用受限。近年来,深度学习算法在计算机视觉方向的应用取得了较好的成果,针对多光谱遥感影像语义分割问题,使用深度学习的语义分割方法来实现遥感影像的高性能分割,在U-Net网络结构上添加激活层、Dropout层、卷积层,提出一种基于U-Net优化的深度卷积神经网络,在少量数据集的基础上实现了对以SAR图像合成的多光谱影像中耕地、建筑、河流的快速检测,整体分割准确率达94.6%。与U-Net,SegNet的对照实验结果表明,所提方法的分割准确率相比U-Net,SegNet整体较优,相比U-Net和SegNet分别提升了2.5%与5.8%。
中图分类号:
[1]BEUMIER C,IDRISSA M.Building change detection from uniform regions[C]//Proceedings of the 17th Iberoamerican Congress Pattern Recognition,Images Analysis,Computer Vision,and Applications.Buenos Aires,Argentina:Springer,2012:648-655. [2]TURKER M,SUMER E.Building-based damage detection due to earthquake using the watershed segmentation of the postevent aerial images[J].International Journal of Remote Sensing,2008,29(11):3073-3089. [3]ZHOU Z M,MENG Y,HUANG S X,et al.Building segmenta-teon of spaceborne SAR images based on energy minimization[J].Acta Automatica Sinica,2016,42(2):279-289. [4]LI W M,WU Y H,HU Z Y.Urban change detection underlarge view and illumination variations[J].Acta Automatica Sini-ca,2009,35(5):449-461. [5]LUKASHEVICH P,ZALESSKY B,BELOTSERKOVSKY A.Building detection on aerial and space images[C]//Proceedings of the 2017 International Conference on Information and Digital Technologies (IDT).Zilina,Slovakia:IEEE,2017:246-251. [6]HINTON G,SALAKHUTDINOV R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507. [7]LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Na-ture,2015,521(7553):436-444. [8]HE K,GKIOXARI G,DOLLAR P,et al.Mask R-CNN[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2020,42(2):386-397. [9]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[J].Advances in Neural Information Processing Systems,2015,25(2). [10]FARFADE S S,SABERIAN M,LI L J.Multi-view Face Detection Using Deep Convolutional Neural Networks[C]//International Conference on Multimedia Retrieval.2015:643-650. [11]SHELHAMER E,LONG J,DARRELLT.Fully Convolutional Networks for Semantic Segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(4):640-651. [12]BADRINARAYANAN V,KENDALL A,CIPOLLA R.Segnet:A deep convolutional encoder-decoder architecture for scene segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(12):2481-2495. [13]SIMONYAN K,ZISSERMAN A.Very deep convolutional net-work for large-scale image recognition[C]//3rd International Conference on Learning Representations(ICLR).Hilton San Diego:Computer Science,2015:1150-1210. [14]IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing Internal covariate shift[J/OL].arXiv:Learning,2015.[2019-09-25].https://arxiv.org/abs/1502.03167. [15]RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional Networks for Biomedical ImageSegmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer International Publishing,2015. [16]CHEN Z P,DENG P,ZHONG J S,et al.Application of Texture Features in SAR Image Change Detection[J].Remote Sensing Technology and Application,2002,17(3):162-166. [17]CHEN S,WANG H.SAR target recognition based on deeplearning[C]//2014 International Conference on Data Science and AdvancedAnalytics.2014. [18]GONG M,LI Y,JIAO L,et al.SAR change detection based on intensity and texture changes[J].Isprs Journal of Photogrammetry & Remote Sensing,2014,93(93):123-135. [19]WEI G,GUO H,AN J B,et al.SAR sea surface oil spill image segmentation method based on fully convolutional neural network[J].Journal of Computer Applications,2019(S1). [20]GUO X.Water change detection based on pixel-level fusion of optics and SAR image[D].Xuzhou:China University of Mining and Technology. [21]EMEK R A,DEMIR N.Building detection from sar images using unet deep learning method[J/OL].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2020:215-218.https://www.researchgate.net/publication/347118757_BUILDING_DETECTION_FROM_SAR_IMAGES_USING_UNET_DEEP_LEARNING_METHOD. [22]YANG D H,MA D B.Optimization Algorithm of Polarization Lee Filter Based on Polarization Vector Similarity Coefficients[J].Journal of Information Engineering University,2010,11(6):737-740. [23]ZHANG H,LING C.Multichannel simultaneous dual-band fully polarimetric airborne synthetic aperture radar:System features and experimental results[J].Journal of Applied Remote Sensing,2018,12(3):1. [24]WU B,LIN S S,ZHOU G J.Object-oriented high-resolution remote sensing image segmentation and classification evaluation index[J].Journal of Geo-Information Science,2013,15(4):567-573. |
[1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[5] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[6] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[7] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[13] | 王君锋, 刘凡, 杨赛, 吕坦悦, 陈峙宇, 许峰. 基于多源迁移学习的大坝裂缝检测 Dam Crack Detection Based on Multi-source Transfer Learning 计算机科学, 2022, 49(6A): 319-324. https://doi.org/10.11896/jsjkx.210500124 |
[14] | 楚玉春, 龚航, 王学芳, 刘培顺. 基于YOLOv4的目标检测知识蒸馏算法研究 Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 计算机科学, 2022, 49(6A): 337-344. https://doi.org/10.11896/jsjkx.210600204 |
[15] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
|