基于无人机多光谱影像的地物识别

doi:10.6048/j.issn.1001-4330.2020.05.018

新疆农业科学 ›› 2020, Vol. 57 ›› Issue (5): 932-939.DOI: 10.6048/j.issn.1001-4330.2020.05.018

基于无人机多光谱影像的地物识别

魏青, 张宝忠, 魏征

中国水利水电科学研究院流域水循环模拟与调控国家重点实验室国家节水灌溉北京工程技术研究中心,北京 100038

收稿日期:2019-12-20 发布日期:2020-04-23
通信作者: 张宝忠(1981-),男,山西太原人,教授级高工,研究方向为节水灌溉理论与技术,(E-mail)zhangbaozhong333@163.com
作者简介:魏青(1995-),女,河南商丘人,硕士研究生,研究方向为节水灌溉理论与技术,(E-mail)weiqing1221@126.com
基金资助:
国家重点研发计划(2017YFC0403202);优秀青年科学基金(51822907);中国水利水电科学研究院基本科研业务费专项(01881910)

Research on Object Recognition Based on UAV Multispectral Image

WEI Qing, ZHANG Baozhong, WEI Zheng

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research/National Center for Efficient Irrigation Engineering and Technology Research, Beijing 100038, China

Received:2019-12-20 Published:2020-04-23
Correspondence author: ZHANG Baozhong(1981-),male,Taiyuan City,Shanxi Province, professorate senior engineer, Research areas include principle and technology of water-saving irrigation,(E-mail)zhangbaozhong333@163.com
Supported by:
The National Key R & D Program Project (2017YFC0403202); Excellent Youth Science Foundation (51822907); Fundamental Research Business Expenses of China Institute of Water Resources and Hydropower Research (01881910)

摘要/Abstract

摘要： 【目的】利用2018年5和6月获取的无人机多光谱影像对北京市大兴试验基地的部分农田进行地物类型提取研究。【方法】确定感兴趣地物种类,对影像进行时相与光谱特征分析,然后确定归一化植被指数NDVI、归一化绿蓝差异指数NGBDI、修正型比值植被指数MSR和红边波段反射率可以作为最优分类特征,通过基于光谱变量阈值分割的决策树分类法,实现地物分类,并提取种植面积,选取基于目视解译的地面调查数据进行方法验证。【结果】基于时相与光谱特征的决策树分类方法有较好效果,该方法用于小麦、果树和大棚的提取,误差值分别为10.68%、6.06%和16.48%,面积提取误差在17%以内,对无人机多光谱遥感影像进行地物识别具有一定的适用性。【结论】无人机低成本、高效率的优势为农田信息及时获取提供参考。

关键词: 无人机; 多光谱遥感; 时相与光谱特征; 地物识别; 决策树分类法

Abstract: 【Objective】 In view of the lack of timeliness of farmland information acquisition and the difficulty of grasping basic farmland information in time, in this project, the UAV multi-spectral images acquired in May and June 2018 were used to study the extraction of some farmland types in Daxing experimental base in Beijing. 【Method】Firstly, the species of interest were identified, and the temporal and spectral characteristics of the image were analyzed. Then, the normalized vegetation index NDVI, normalized green-blue difference index NGBDI, modified ratio vegetation index MSR and red-band reflectance were determined as the optimal classification features, and the image was segmented by threshold based on spectral variables. The decision tree classification method based on visual interpretation was used to realize the classification of land features and extract the planting area. The method was validated by selecting the ground survey data based on visual interpretation. 【Result】 The results showed that the decision tree classification method based on temporal and spectral characteristics had good effect and the method was applicable to extracting wheat, fruit trees and big shed with errors of 10.68%, 6.06% and 16.48%, respectively. Besides, the area extraction error was within 17%, so we can safely say that UAV multi-spectral remote sensing image has certain applicability for ground object recognition. 【Conclusion】 The advantages of UAV in low cost and high efficiency provide reference for timely access to farmland information.

Key words: UAV; multi-spectral remote sensing; temporal and spectral features; terrain recognition; decision tree classification

中图分类号:

S512
S519

魏青, 张宝忠, 魏征. 基于无人机多光谱影像的地物识别[J]. 新疆农业科学, 2020, 57(5): 932-939.

WEI Qing, ZHANG Baozhong, WEI Zheng. Research on Object Recognition Based on UAV Multispectral Image[J]. Xinjiang Agricultural Sciences, 2020, 57(5): 932-939.

参考文献

[1] 吴梦红,杨长保,林楠.高分辨率遥感影像在土地覆被变化监测中的应用[J].科学技术与工程,2017,17(22):100-106.
WU Menghong, YANG Changbao, LIN Nan. Application of High-spatial Remote Sensing Image in Land Cover Change Monitoring [J]. Science Technology and Engineering, 2017,17(22):100-106.
[2] 田明璐,班松涛,袁涛,等.基于低空无人机多光谱遥感的水稻倒伏监测研究[J].上海农业学报,2018,34(6):88-93.
TIAN Minglu,BAN Songtao,YUAN Tao, et al. Monitoring of lodged rice using low-altitude UAV based multispectral image [J]. Acta Agriculturae Shanghai, 2018,34(6):88-93.
[3] 史舟,梁宗正,杨媛媛,等.农业遥感研究现状与展望[J].农业遥感研究现状与展望[J].农业机械学报,2015,46(2):247-260.
SHI Zhou, LIANG Zongzheng, YANG Yuanyuan, et al. Status and Prospect of Agricultural Remote Sensing [J]. Transactions of the Chinese Society for Agricultural Machinery, 2015,46(2):247-260.
[4] 刘佳,王利民,杨福刚,等.基于HJ时间序列数据的农作物种植面积估算[J].农业工程学报,2015,31(3):199-206.
LIU Jia,WANG Liming,YANG Fugang, et al. Remote sensing estimation of crop planting area based on HJ time-series images [J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(3):199-206.
[5] 李颖,陈秀万,段红伟,等.多源多时相遥感数据在冬小麦识别中的应用研究[J].地理与地理信息科学,2010,26( 4) : 47-49.
LI Ying, CHEN Xiuwan , DUAN Hongwei, et al. Application of Multi-source and Multi-temporal Remote Sensing Data in Winter Wheat Identification [J]. Geography and Geo-Information Science, 2010,26( 4) : 47-49.
[6] 牛亚晓,张立元,韩文霆,等.基于无人机遥感与植被指数的冬小麦覆盖度提取方法[J].农业机械学报,2018,49(4):212-221.
NIU Yaxiao, ZHANG Liyuan, HAN Wenting, et al. Fractional Vegetation Cover Extraction Method of Winter Wheat Based on UAV Remote Sensing and Vegetation Index [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(4):212-221.
[7] 闵文芳,江朝晖,李想,等.基于无人机平台和图像分析的田间作物检测[J].湖南大学学报(自然科学版),2017,43(2):212-216.
MIN Wengfang, JIANG Chaohui, LI Xiang, et al. Area measurement for field crops based on UAV platform and image analysis [J]. Journal of Hunan Agricultural University (Natural Sciences Ed.), 2017,43(2):212-216.
[8] Friederike G, Urs S. Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs) [J]. Remote Sensing, 2017, 9(6):544-.
[9] Mitch, Bryson, Alistair, et al. Airborne vision-based mapping and classification of large farmland environments [J]. Journal of Field Robotics, 2010, 27(5):632-655.
[10] 韩文霆,李广,苑梦婵,等.基于无人机遥感技术的玉米种植信息提取方法研究[J].农业机械学报,2017,48(1):139-147.
HAN Wenting, LI Guang, YUAN Mengchan, et al. Extraction Method of Maize Planting Information Based on UAV Remote Sensing Technology [J]. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(1):139-147.
[11] 戴建国,张国顺,郭鹏,等.基于无人机遥感可见光影像的北疆主要农作物分类方法[J].农业工程学报,2018,34(18):122-129.
DAI Jianguo, ZHANG Guoshun, GUO Peng, et al. Classification method of main crops in northern Xinjiang based on UAV visible waveband images [J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(18):122-129.
[12] Garcia-Ruiz F, Sankaran S, Maja J M, et al. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees[J]. Computers and Electronics in Agriculture, 2013, 91:106-115.
[13] 戴建国,张国顺,郭鹏,等.基于无人机遥感多光谱影像的棉花倒伏信息提取[J].农业工程学报,2019,35(2):63-70.
DAI Jianguo, ZHANG Guoshun, GUO Peng, et al. Information extraction of cotton lodging based on multi-spectral image from UAV remote sensing [J]. Transactions of the Chinese Society of Agricultural Engineering, 2019,35(2):63-70.
[14] 崔美娜,戴建国,王守会,等.基于无人机多光谱影像的棉叶螨识别方法[J].新疆农业科学,2018,55(8):1457-1466.
CUI Meina,DAI Jianguo,WANG Shouhui, et al. Research on Identification Method of Mite Infection Cotton Based on of UAV Multi-Spectral Image [J]. Xinjiang Agricultural Sciences, 2018,55(8):1457-1466.
[15] Rouse, J.W. Monitoring vegetation systems in the great plains with erts [J]. Nasa Special Publication, 1974, (351):309-317.
[16] Gamon, J. A, Penuelas, J, & Field, C.B.A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency [J].Remote Sensing of Environment, 1992, 41(1):35-44.
[17] Verrelst J, Schaepman M E, Koetz B, et al. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data [J].Remote Sensing of Environment, 2008,112(5):2341-2353.
[18] Chen, J. M. Evaluation of vegetation indices and a modified simple ratio for boreal application [J]. Canadian Journal of Remote Sensing, 1996, 22(3):229-242.
[19] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003.
ZHAO Yingshi. Principle and Method of Remote Sensing Applied Analysis [M]. Beijing: Science Reading, 2003.

基于无人机多光谱影像的地物识别

Research on Object Recognition Based on UAV Multispectral Image

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