基于词向量融合的遥感场景零样本分类算法

doi:10.11896/jsjkx.181202257

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

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

(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 Chen^1,3, YUAN Yu-wei², WANG Hong-wei³, LIU Yu³, LIU Si-tong⁴, QUAN Ji-cheng³

(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/Abstract

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

吴晨, 袁昱纬, 王宏伟, 刘宇, 刘思彤, 全吉成. 基于词向量融合的遥感场景零样本分类算法[J]. 计算机科学, 2019, 46(12): 286-291.

WU Chen, YUAN Yu-wei, WANG Hong-wei, LIU Yu, LIU Si-tong, QUAN Ji-cheng. Word Vectors Fusion Based Remote Sensing Scenes Zero-shot Classification Algorithm[J]. Computer Science, 2019, 46(12): 286-291.

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基于词向量融合的遥感场景零样本分类算法

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

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