摘要: |
【目的】为应用浅层结构的机器学习分类器和高空间分辨率影像实现休耕区绿肥、粮食及经济作物快速准确分类。【方法】利用分辨率为5m的Rapideye影像,以云南省石林县部分休耕试点区为研究区,使用Softmax浅层机器学习分类器对研究区内绿肥作物、水稻、玉米及烟草等4种典型作物进行遥感识别与空间信息提取,并以极大似然分类法为参照,通过地面样方数据验证该方法的精度。【结果】基于Softmax方法的4种典型作物分类的总体精度和Kappa系数分别为85.98%和0.8157,比极大似然分类高4.59%和0.0617;绿肥、水稻、烟草的生产者精度和用户精度均达到84%以上,玉米的则低于75%,原因是由于绿肥、水稻、烟草3种作物种植较为集中,而玉米种植地块面积小且极为分散;绿肥与烟草错分问题较明显,影响因素为“同物异谱、异物同谱”。【结论】基于Softmax的浅层机器学习分类器提高了分类精度,该文研究结果可为使用浅层机器学习方法快速准确掌握休耕情况提供参考。 |
关键词: 遥感 Softmax分类器 休耕 作物分类 |
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基金项目:四川省科技厅软科学研究项目“基于高分六号遥感影像的四川粮食作物布局研究”(2019JDR0121);四川省科技厅应用基础研究项目“基于空间大数据的乡村地区土地利用变化研究”(2019YJ0608);四川省重点研发项目“基于物联网+遥感技术的智慧农业研究”(2017GZ0160);四川省省院省校合作项目“基于大数据机器学习与冠层反射率模型结合的水稻叶面积指数提取技术”(2018JZ0054);四川省应用基础研究项目“基于互联网+多阶段遥感反演的区域水稻参数逐田块监测技术研究”(2017JY0284);四川省财政创新能力提升工程青年基金“基于冠层反射率模型多阶段反演的逐地块水稻参数采集技术研究”(2017QNJJ-023) |
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Classification Of Summer Crops In Fallow Region With Shallow Learning |
Dong Xiuchun
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Remote Sensing Application Institute
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Abstract: |
[Purpose] Fallow monitoring is an important part of the fallow pilot project, which cooperates with the Ministry of Agriculture and Rural Affair. By the method of remote sensing, judge the land is fallow or planting green manure which is used to protect the land capability. The monitoring result is of great significance for the management department to master the situation of fallow region. To achieve fast and accurate classification of multiple crops, including green manure, grain and cash crops in fallow region by machine learning classifier with shallow structure and high spatial resolution remote sensing image. Aim to provide reference for mastering the situation in fallow region with the method of the shallow learning. [Methods] In this paper, a part of the fallow pilot region in Shilin county was selected as the study area, the Rapideye image with the resolution of 5m was adopted, based on Softmax classifier which belongs to machine learning of shallow structure, the recognition and spatial information extraction of four typical crops, such as green manure, rice, corn and tobacco were carried out in the study area. By the data of in-situ survey, the accuracy was evaluated and compared with the result of maximum likelihood. [Results] The results revealed that, the overall accuracy and Kappa coefficients of the classification based on Softmax classifier were 85.98% and 0.8157, respectively, slightly higher than the maximum likelihood of 81.39% and 0.7540. Their producer precision and user precision of green manure, rice and tobacco were higher than 84%, while corn was less than 75%. The reason was that the planting of green manure, rice and tobacco are relatively concentrated, while the planting of corn was smaller and scattered. There were obvious classification errors, because of the confusion between green manure and tobacco.[Conclusion]The results indicate that Softmax classifier can improve the accuracy of multiple crops classification in fallow region.This method can provide reference for the application of shallow machine learning in fallow region. |
Key words: remote sensing Softmax classifier fallow crop classification |