计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 286-291.doi: 10.11896/jsjkx.181202257

• 图形图像与模式识别 • 上一篇    下一篇

基于词向量融合的遥感场景零样本分类算法

吴晨1,3, 袁昱纬2, 王宏伟3, 刘宇3, 刘思彤4, 全吉成3   

  1. (中国人民解放军海军航空大学 山东 烟台264001)1;
    (91977部队 北京102200)2;
    (中国人民解放军空军航空大学 长春130022)3 ;
    (西安飞行学院 西安710000)4
  • 收稿日期:2018-12-04 出版日期:2019-12-15 发布日期:2019-12-17
  • 通讯作者: 全吉成(1960-),男,博士,教授,主要研究方向为三维虚拟现实,E-mail:jicheng_quan@126.com。
  • 作者简介:吴晨(1991-),男,博士生,主要研究方向为遥感图像场景分类,E-mail:wuchen_research@aliyun.com;袁昱纬(1988-),男,博士,工程师,主要研究方向为遥感图像智能识别;王宏伟(1984-),男,博士生,讲师,主要研究方向为遥感图像智能识别;刘宇(1968-),男,博士,副教授,主要研究方向为遥感图像解译;刘思彤(1990-),女,硕士,讲师,主要研究方向为遥感图像处理。
  • 基金资助:
    本文受国家青年自然科学基金(61301233)资助。

Word Vectors Fusion Based Remote Sensing Scenes Zero-shot Classification Algorithm

WU Chen1,3, YUAN Yu-wei2, WANG Hong-wei3, LIU Yu3, LIU Si-tong4, QUAN Ji-cheng3   

  1. (Naval Aeronautical University,Yantai,Shandong 264001,China)1;
    (The 91977 of PLA,Beijing 102200,China)2;
    (Aviation University Air Force,Changchun 130022,China)3;
    (Xi’an Flight Academy of PLA,Xi’an 710000,China)4
  • Received:2018-12-04 Online:2019-12-15 Published:2019-12-17

摘要: 零样本分类算法无须标注要识别的类别样本,因而能大幅度降低实际应用成本,近年来受到广泛关注。遥感场景类别的语义词向量与图像特征空间原型的结构不一致问题,严重影响了遥感场景零样本的分类效果。利用不同词向量间的互补性,文中提出一种基于语义词向量融合的遥感场景零样本分类算法,即耦合式解析字典学习(Coupled Analysis Dictionary Learning,CADL)方法。首先,采用稀疏编码效率较高的解析字典学习方法获取各语义词向量的稀疏系数,以减少冗余信息;然后,将对应的稀疏编码系数串接后作为融合语义词向量表示,并将融合语义词向量线性映射到图像特征空间,与图像特征空间场景类别原型表示进行结构对齐,以降低结构差异性;最后,计算得到要识别的场景类别的图像特征原型,并采用最近邻分类器在图像特征空间完成分类。在UCM和AID数据集上对多种语义词向量的融合进行定量实验,同时将RSSCN7数据集作为已知场景类别的数据集来对两幅实际遥感图像进行定性实验。在UCM和AID上的定量实验分别获得了最高总体分类准确度48.40%和60.23%,相比于典型零样本分类方法的总体分类准确度分别提升了4.80%和6.98%。对两幅实际遥感图像的定性实验,同样获得了最佳零样本的分类效果。实验结果表明,多种语义词向量融合,可以获得与图像特征空间原型结构更一致的语义词向量,且显著提升了遥感场景零样本分类的准确度。

关键词: 遥感场景分类, 零样本分类, 结构对齐, 词向量融合, 解析字典学习

Abstract: Zero-shot classification algorithm does not need to label the sample of unseen classes to be recognized,so it can greatly reduce the cost of practical application,which has attracted wide attention in recent years.The problem of structure difference between word vectors and image feature prototypesseriously affects the zero-shot classification performance of remote sensing scenes.Based on the complementarity among different kinds of word vectors,the remote sensing scenes zero-shot classification algorithm based on word vectors fusion,named coupled analysis dictionary lear-ning method,was proposed.Firstly,the sparse coefficients of different kinds of word vectors are obtained by the more efficient analysis dictionary learning to reduce the redundant information.Then,the sparse coefficients are concatenated and denoted as the fused word vectors,and a structure alignment operation is performed based on the image feature prototypes to reduce structural differences by embedding the fused word vectors into image feature space.Finally,the image feature prototypes of the scene classes unseen are calculated,and the nearest neighbor classifier is employed to complete the classification in the image feature space.Quantitative experiments of the fusion of multiple semantic word vectors were carried out on UCM and AID datasets.At the same time,two real remote sensing images were qualitatively tested with RSSCN7 datasets as the seen dataset.Auantitative experiments obtaines the highest overall classification accuracies of 48.40% and 60.23% on UCM and AID,which respectively exceeds the typical comparative methods by 4.80% and 6.98%.In qualitative experiments on two real remote sensing images ,the algorithm also obtaines the best zero-shot classification performance.The experimental results show that the fused word vectors are more consistent with the prototypes in image feature space,and the zero-shot classification accuracies of remote sensing scenes can be significantly improved.

Key words: Remote sensing scenes classification, Zero-shot classification, Structure alignment, Word vectors fusion, Analysis dictionary learning

中图分类号: 

  • TP181
[1] CHEN S,TIAN Y L.Pyramid of spatial relations for scene-level land use classification[J].IEEE Transactions on Geoscience & Remote Sensing,2014,53(4):1947-1957.
[2] LI A,LU Z,WANG L,et al.Zero-shot scene classification for high spatial resolution remote sensing images[J].IEEE Tran-sactions on Geoscience & Remote Sensing,2017,55(7):4157-4167.
[3] XIAN Y Q,AKATA Z,SHARMA G,et al.Latent embeddings for zero-shot classification[C]//Proceedings of Computer Vision and Pattern Recognition.LA USA:IEEE,2016:69-77.
[4] WANG D,LI Y,LIN Y,et al.Relational Knowledge Transfer for Zero-Shot Learning[C]//Proceedings of AAAI.CA USA:2016,2-7.
[5] ZHANG Z,SALIGRAMA V.Zero-shot learning via joint latent similarity embedding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.LA USA:IEEE,2016:6034-6042.
[6] ZHANG Z,SALIGRAMA V.Zero-shot learning via semantic similarity embedding[C]//Proceedings of the IEEE InternationalConference on Computer Vision.Boston USA:IEEE,2015:4166-4174.
[7] WANG Q,CHEN K.Zero-shot visual recognition via bidirec- tional latent embedding[J].International Journal of Computer Vision,2017,124(3):356-383.
[8] LI Y,WANG D,HU H,et al.Zero-shot recognition using dual visual-semantic mapping paths[J].arXiv:1703.05002,2017.
[9] ZHAO B,WU B,WU T,et al.Zero-shot learning posed as a missing data problem[J].arXiv:1612.00560,2016.
[10] LAMPERT C H,NICKISCH H,HARMELING S.Attribute- based classification for zero-shot visual object categorization[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,36(3):453-465.
[11] SOCHER R,GANJOO M,MANNING C D,et al.Zero-shot learning through cross-modal transfer[C]//Proceedings of Advances in Neural Information Processing Systems.Lake Tahoe USA,ACM 2013:935-943.
[12] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[13] SOCHER R,MANNING C D.Glove:Global vectors for word representation[C]//Empirical Methods in Natural Language Processing (EMNLP).2014:1532-1543
[14] YANG M,CHANG H Y,LUO W X.Discriminative analysis-synthesis dictionary learning for image classification[J].Neurocomputing,2017,219:404-411.
[15] WANG J J,GUO Y Q,GUO J,et al.Synthesis linear classifier-based analysis dictionary learning for pattern classification [J].Neurocomputing,2017,238:103-113.
[16] RAVISHANKAR S,BRESLER Y.L0 sparsifying transform learning with efficient optimal updates and convergence guarantees[J].IEEE Transactions on Signal Processing,2015,63(9):2389-2404.
[17] YANG Y,NEWSAM S.Bag-of-visual-words and spatial extensions for land-use classification[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances In Geographic Information Systems.ACM,2010:270-279.
[18] XIA G S,HU J,HU F,et al.AID:A benchmark dataset for performance evaluation of aerial scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(7):3965-3981.
[19] ZOU Q,NI L,ZHANG T,et al.Deep learning based feature selection for remote sensing scene classification[J].IEEEGeo-science & Remote Sensing Letters,2015,12(11):2321-2325.
[20] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[1] 薛占熬, 张敏, 赵丽平, 李永祥. 集对优势关系下多粒度决策粗糙集的可变三支决策模型[J]. 计算机科学, 2021, 48(1): 157-166.
[2] 李亚男, 胡宇佳, 甘伟, 朱敏. 基于深度学习的miRNA靶位点预测研究综述[J]. 计算机科学, 2021, 48(1): 209-216.
[3] 顾秋阳, 琚春华, 吴功兴. 融入深度自编码器与网络表示学习的社交网络信息推荐模型[J]. 计算机科学, 2020, 47(11): 101-112.
[4] 张永安, 颜斌斌. 一种股票市场的深度学习复合预测模型[J]. 计算机科学, 2020, 47(11): 255-267.
[5] 唐雷明, 白沐尘, 何星星, 黎兴玉. 基于命题逻辑的完全标准矛盾体及最小标准矛盾体[J]. 计算机科学, 2020, 47(11A): 83-85.
[6] 纪明轩, 宋玉蓉. 一种基于对数位置表示和自注意力的机器翻译新模型[J]. 计算机科学, 2020, 47(11A): 86-91.
[7] 郑添健, 侯金宏, 张维, 王驹. 循环描述逻辑系统FL0最大不动点模型的有穷基[J]. 计算机科学, 2020, 47(11A): 92-96.
[8] 王赛男, 郑雄风. 基于边缘计算的图像语义分割应用与研究[J]. 计算机科学, 2020, 47(11A): 276-280.
[9] 周玉, 任钦差, 牛会宾. 训练样本数据选择方法研究综述[J]. 计算机科学, 2020, 47(11A): 402-408.
[10] 王晓晖, 张亮, 李俊清, 孙玉翠, 田捷, 韩睿毅. 基于遗传算法与随机森林的XGBoost改进方法研究[J]. 计算机科学, 2020, 47(11A): 454-458.
[11] 马创, 周代棋, 张业. 基于改进鲸鱼算法的BP神经网络水资源需求预测方法[J]. 计算机科学, 2020, 47(11A): 486-490.
[12] 赵霞, 李娴, 张泽华, 张晨威. 结合社区嵌入和节点嵌入的社区发现算法[J]. 计算机科学, 2020, 47(10): 121-125.
[13] 魏霖静, 宁璐璐, 郭斌, 侯振兴, 甘诗润. 基于混合蛙跳算法的K-mediods聚类挖掘与并行优化[J]. 计算机科学, 2020, 47(10): 126-129.
[14] 陈玉金, 徐吉辉, 史佳辉, 刘宇. 基于直觉犹豫模糊集的三支决策模型及其应用[J]. 计算机科学, 2020, 47(8): 144-150.
[15] 董明刚, 黄宇扬, 敬超. 基于遗传实例和特征选择的K近邻训练集优化方法[J]. 计算机科学, 2020, 47(8): 178-184.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[2] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[3] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[4] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[5] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[6] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[7] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[8] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[9] 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案[J]. 计算机科学, 2018, 45(4): 122 -125 .
[10] 杨羽琦,章国安,金喜龙. 车载自组织网络中基于车辆密度的双簇头路由协议[J]. 计算机科学, 2018, 45(4): 126 -130 .