[1]李婵,王俊杰,邬国锋,等.基于叶片光谱特征的农业区域植物分类[J].深圳大学学报理工版,2018,35(3):307-315.[doi:10.3724/SP.J.1249.2018.03307]
　LI Chan,WANG Junjie,WU Guofeng,et al.Classification of agricultural plants based on leaf spectral features[J].Journal of Shenzhen University Science and Engineering,2018,35(3):307-315.[doi:10.3724/SP.J.1249.2018.03307]
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基于叶片光谱特征的农业区域植物分类()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第35卷
期数:
2018年第3期
页码:
307-315
栏目:
电子与信息科学
出版日期:
2018-05-15

文章信息/Info

Title:
Classification of agricultural plants based on leaf spectral features
文章编号:
201803010
作者:
李婵1; 2; 王俊杰1; 邬国锋1; 李清泉1
1） 深圳大学海岸带地理环境监测国家测绘地理信息局重点实验室，广东深圳 518060
2） 深圳大学信息工程学院，广东深圳 518060
Author(s):
LI Chan1; 2;  WANG Junjie1;  WU Guofeng1;  and LI Qingquan1
1) Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
2) College of Information Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
关键词:
地物波谱学; 高光谱遥感; 植物分类; 叶片光谱; 主成分分析; 机器学习;  k最近邻; 支持向量机; 随机森林
Keywords:
object spectroscopy;  hyperspectral remote sensing;  vegetation classification;  leaf spectrum;  principal component analysis;  machine learning;  k-nearest-neighbors;  support vector machine;  random forest
分类号:
TP 79
DOI:
10.3724/SP.J.1249.2018.03307
文献标志码:
A
摘要:
基于农业区域8种植物的叶片光谱数据，提取63种光谱特征变量，并对全波段光谱（原始、一阶微分和包络线去除光谱）提取主成分，分别采用k最近邻（k-nearest-neighbors，kNN）、 支持向量机（support vector machine，SVM）和随机森林（random forest，RF）3种机器学习方法对不同植物进行遥感分类．比较3种方法所得的总精度、训练精度、验证精度及8种植物的生产者精度．结果表明，SVM的分类性能优于kNN与RF；单一的光谱特征变量识别精度都较低（<50%）；基于主成分分析的一阶微分光谱识别性能优于原始光谱和包络线去除光谱．研究指出，叶片一阶微分光谱与SVM相结合的方法能够准确识别不同植物物种．可为景观或区域尺度的植被遥感分类、精准农业和森林资源调查等提供借鉴．
Abstract:
With the leaf hyperspectral data of eight plant species in the agricultural region, this paper aims to extract 63 spectral characteristic variables and principal components derived from three types of full-spectrum (the original, the first derivative and the continuum-removed reflectance spectrum). We employ three machine learning methods (k-nearest-neighbors, kNN; support vector machine, SVM; random forest, RF) in the remote classification of different plants. Based on the comprehensive comparisons of the overall accuracy, training accuracy, test accuracy and species producer’s accuracy, we find that SVM outperforms kNN and RF in vegetation classification, and single spectral characteristic variable has relatively weak classification accuracy (<50%). Moreover, the classification models with the PCA-based first derivative reflectance outperforms those with the PCA-based original and continuum-removed reflectance. This study demonstrates that the combination of leaf-level first derivative reflectance and SVM method can accurately identify different plant species and provide the method and theory basis for the remote classification of vegetation, precision agriculture and forest resource inventory at the landscape or region level.

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备注/Memo

备注/Memo:
Received：2018-02-28；Accepted：2018-03-09
Foundation：National Natural Science Foundation of China（41601362）
Corresponding author：Professor LI Qingquan．E-mail: liqq@szu.edu.cn
Citation：LI Chan，WANG Junjie，WU Guofeng，et al．Classification of agricultural plants based on leaf spectral features［J］. Journal of Shenzhen University Science and Engineering, 2018, 35(3): 307-315.（in Chinese）
基金项目：国家自然科学基金资助项目（41601362）
作者简介：李婵（1992—），女，深圳大学硕士研究生．研究方向：高光谱遥感植被分类．E-mail：13728817048@163.com
引文：李婵，王俊杰，邬国锋，等．基于叶片光谱特征的农业区域植物分类［J］. 深圳大学学报理工版，2018，35(3)：307-315.
更新日期/Last Update: 2018-04-28