| 融合城市行道树特征选取模型的自适应深度学习分类 |
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| 引用本文： | 乔莲花,刘民士.融合城市行道树特征选取模型的自适应深度学习分类[J].测绘通报,2020,0(6):77-80. |
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| 作者姓名： | 乔莲花 刘民士 |
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| 作者单位： | 1. 南京市测绘勘察研究院股份有限公司, 江苏 南京 210019;2. 南京师范大学地理科学学院, 江苏 南京 210023 |
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| 基金项目： | 国家自然科学基金（41601499）；南京市测绘勘察研究院股份有限公司科研项目（2019RD03） |
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| 摘 要： | 针对城市行道树的学习多分类问题，本文在综合分析城市行道树多分类特征的基础上，提出一种融合特征自动选取模型的自适应深度学习方法。基于随机森林法，学习行道树的特征重要性，通过特征消除方法舍弃不重要的特征，实现城市行道树多分类特征自动选取；在城市行道树分类特征工程提取的基础上，构建了城市行道树多分类问题的自适应深度学习方法，并采用交叉验证与参数搜索方法，对所提出的深度学习模型进行改进。试验结果表明，本文所提出的融合特征自动选取模型的自适应深度学习方法具有良好性能，解决了城市行道树多分类预测的准确性与泛化问题。
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| 关 键 词： | 城市行道树分类 数据表示 特征工程 深度学习 模型评估与优化 |
| 收稿时间： | 2019-11-24 |
| 修稿时间： | 2020-01-16 |
A feature selection-based deep learning model for urban street trees classification |
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| QIAO Lianhua,LIU Minshi.A feature selection-based deep learning model for urban street trees classification[J].Bulletin of Surveying and Mapping,2020,0(6):77-80. |
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| Authors: | QIAO Lianhua LIU Minshi |
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| Institution: | 1. Nanjing Research Institute of Surveying and Mapping, Nanjing 210019, China;2. School of Geography, Nanjing Normal University, Nanjing 210023, China |
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| Abstract: | In order to manage urban trees in an efficient way, this paper studies the classification of urban street trees. Aiming at the complex problems such as learning multi-classification model optimization of urban street trees, an adaptive deep learning method is proposed by considering the multi-classification characteristics of urban roadside trees. The feature engineering method based on random forest learning is adopted to calculate and analyze the feature importance of urban roadside trees, and the unimportant features are discarded by recursive feature elimination method. Moreover, to improve the performance of multi-classification learning algorithm for urban street trees, an adaptive deep learning method is further constructed on the basis of urban tree feature learning. Furthermore, the proposed deep learning model is evaluated and improved by cross-validation and parameter search methods. The simulation results show that our proposed algorithm has superior performance and effectively to solve the problem of accuracy and generalization of multi-classification of urban street trees. |
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| Keywords: | urban street trees classification data representation feature engineering deep learning model model evaluation and optimization |
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