[1]郭茂祖,王偲佳,王鹏跃,等.基于卫星图的小样本街区品质评估[J].智能系统学报,2022,17(6):1254-1262.[doi:10.11992/tis.202111049]
GUO Maozu,WANG Sijia,WANG Pengyue,et al.Small sample block quality evaluation based on satellite images[J].CAAI Transactions on Intelligent Systems,2022,17(6):1254-1262.[doi:10.11992/tis.202111049]
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GUO Maozu,WANG Sijia,WANG Pengyue,et al.Small sample block quality evaluation based on satellite images[J].CAAI Transactions on Intelligent Systems,2022,17(6):1254-1262.[doi:10.11992/tis.202111049]
基于卫星图的小样本街区品质评估
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第6期
页码:
1254-1262
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2022-11-05
- Title:
- Small sample block quality evaluation based on satellite images
- Keywords:
- block quality assessment; satellite map; few-shot learning; adaptive subspace; depth neural network; singular value decomposition; unbalanced dataset; under-sampling
- 分类号:
- TP391.41;TP18
- 文献标志码:
- 2022-08-25
- 摘要:
- 量化的城市街区品质评价是街区设计规划的重要依据，图像数据是街区品质评价模型的重要维度。目前的研究中存在街区品质标注成本较高的问题。因此本文改进基于子空间的小样本学习方法，对街区卫星图像特征进行奇异分解生成类别子空间，并将训练集子空间参数继承到街区品质评估模型中。实验结果表明，在小样本街区品质评估问题上，本文方法相比传统小样本学习方法的正确率提高约30%，一致性提高约15%。
- Abstract:
- Quantitative urban block quality evaluation is an important foundation for block design and planning, and image data is an important dimension of the block quality evaluation model. Currently, there are some problems in this field of research, such as the high cost of block quality labeling. This paper improves the small sample learning method based on subspace, performs singular decomposition on the satellite image features of the block to generate class subspace, and inherits the subspace parameters of the training set into the block quality evaluation model. The experimental results show that this method is about 30% more accurate and 15% more consistent than the traditional small sample learning method.
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备注/Memo
收稿日期:2021-11-26。
基金项目:国家自然科学基金面上项目（61871020）；北京市属高校高水平创新团队建设计划项目（IDHT20190506）.
作者简介:郭茂祖，教授，博士生导师，主要研究方向为机器学习、智慧城市、生物信息学。主持和参与国家自然科学基金面上项目、北京市属高校高水平创新团队建设计划项目和北京市教委科技计划重点项目等。吴文俊人工智能科学技术奖获得者。曾获得教育部高等学校科学研究优秀成果自然科学二等奖、省科技进步二等奖等。发表学术论文200余篇;王偲佳，硕士研究生，主要研究方向为城市计算与人工智能;王鹏跃，博士研究生，主要研究方向为城市计算与人工智能
通讯作者:赵玲玲.E-mail:zhaoll@hit.edu.cn
更新日期/Last Update:
1900-01-01