| 基于多核主动学习和多源数据融合的农田塑料覆被分类 |
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| 引用本文： | 冯权泷,牛博文,朱德海,刘逸铭,欧聪,刘建涛.基于多核主动学习和多源数据融合的农田塑料覆被分类[J].农业机械学报,2022,53(2):177-185. |
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| 作者姓名： | 冯权泷 牛博文 朱德海 刘逸铭 欧聪 刘建涛 |
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| 作者单位： | 中国农业大学；自然资源部农用地质量与监控重点实验室;中国移动通信集团广东有限公司;山东建筑大学 |
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| 基金项目： | 国家自然科学基金项目（42001367）和国家重点研发计划项目（2018YFE0122700） |
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| 摘 要： | 通过引入多源多时相卫星遥感数据,提出了一种基于多核主动学习的农田塑料覆被分类算法,实现农业塑料大棚和地膜的精准分类.首先基于多时相Sentinel-1雷达和Sentinel-2光学遥感影像,提取其光谱特征、纹理特征等,以构建多维特征空间.然后构建多核学习模型,实现多源、多时相特征的自适应融合.最后构建基于池的主动学习策...
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| 关 键 词： | 农田塑料覆被 多源遥感数据 多核主动学习 遥感影像分类 |
| 收稿时间： | 2021/1/26 0:00:00 |
Classification of Agricultural Plastic Cover Based on Multi-kernel Active Learning and Multi-source Data Fusion |
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| FENG Quanlong,NIU Bowen,ZHU Dehai,LIU Yiming,OU Cong,LIU Jiantao.Classification of Agricultural Plastic Cover Based on Multi-kernel Active Learning and Multi-source Data Fusion[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(2):177-185. |
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| Authors: | FENG Quanlong NIU Bowen ZHU Dehai LIU Yiming OU Cong LIU Jiantao |
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| Institution: | (College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Key Laboratory for Agricultural Land Quality Monitoring and Control,Ministry of Natural Resources,Beijing 100193,China;China Mobile Group Guangdong Co.,Ltd.,Guangzhou 510623,China;School of Surveying and Geo-Informatics,Shandong Jianzhu University,Ji nan 250101,China) |
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| Abstract: | An agricultural plastic covering classification algorithm was proposed based on multi-kernel active learning to achieve accurate classification of agricultural greenhouses and mulch film by introducing multi-source and multi-temporal satellite remote sensing data, and their spectral features and texture features were firstly extracted to construct a multi-dimensional feature space based on the multi-temporal Sentinel-1 radar and Sentinel-2 optical remote sensing data. And then, a multi-kernel learning model was constructed to realize the adaptive fusion of multi-source and multi-temporal features. Finally, a pool-based active learning strategy is constructed to further improve the generalization ability of the classification model by introducing an elimination mechanism for training samples. The test results showed that the overall accuracy of the proposed classification method was 95.6%, the Kappa coefficient was 0.922. Compared with that of the classic SVM, random forest, KNN, decision tree, AdaBoost model, the accuracy of the active learning model was improved by 5.7, 12.1, 11.4, 22.3 and 10.3 percentage points. And under the same classification accuracy, active learning can reduce more than half of the label data than passive learning. The accuracy was improved by 3.7 and 12.7 percentage points, respectively, compared with using only single phase and single-sensor remote sensing images. The research results showed that multi-kernel active learning can effectively perform multi-sensor and multi-temporal data fusion, and can achieve high classification accuracy under small sample conditions. It can provide model reference for remote sensing monitoring of agricultural plastic cover. |
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| Keywords: | agricultural plastic cover multi-source remote sensing data multi-kernel active learning remote sensing imagery classification |
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