[1]苗荣慧,黄锋华,杨华,等.基于空谱一体化的农田高光谱图像分类[J].江苏农业学报,2018,(04):818-824.[doi:doi:10.3969/j.issn.1000-4440.2018.04.015]
 MIAO Rong-hui,HUANG Feng-hua,YANG hua,et al.Farmland classification of hyperspectral image based on spatial-spectral integration method[J].,2018,(04):818-824.[doi:doi:10.3969/j.issn.1000-4440.2018.04.015]
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基于空谱一体化的农田高光谱图像分类()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年04期
页码:
818-824
栏目:
耕作栽培·资源环境
出版日期:
2018-08-25

文章信息/Info

Title:
Farmland classification of hyperspectral image based on spatial-spectral integration method
作者:
苗荣慧1黄锋华1杨华1邓雪峰1陈晓倩2
(1.山西农业大学信息科学与工程学院,山西太谷030801;2.西北农林科技大学信息工程学院,陕西杨凌712100)
Author(s):
MIAO Rong-hui11HUANG Feng-hua1YANG hua1DENG Xue-feng1CHEN Xiao-qian2
(1. College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China;2.College of Information Engineering, Northwest A&F University, Yangling 712100, China)
关键词:
高光谱图像空谱一体化农田图像分类主成分分析支持向量机
Keywords:
hyperspectral imagespatial-spectral integrationfarmland image classificationprincipal component analysis (PCA)support vector machine (SVM)
分类号:
TP751;TP391
DOI:
doi:10.3969/j.issn.1000-4440.2018.04.015
文献标志码:
A
摘要:
为强化高光谱成像技术在近地农业方面的应用,以农田近红外高光谱图像为研究对象,利用高光谱成像技术,结合光谱分析方法和监督分类方法,对农田图像进行分类。针对高光谱图像数据量大、非线性等特点,采用主成分分析(PCA)和支持向量机(SVM)法建立农田图像分类器。在利用光谱信息分类的基础上,采用空谱一体化方法对光谱分类结果进行修正,去除孤立点和噪声的影响。基于支持向量机的总体分类精度为88.4%,采用空谱一体化方法的总体分类精度最高达89.7%,说明利用空间信息修正光谱信息可以提高近地农田对象的分类精度,为基于高光谱图像的近地农田识别提供理论依据。
Abstract:
In order to intensify the application of hyperspectral imaging technology in near field agriculture, near-infrared hyperspectral images were selected as research objects, and the hyperspectral imaging technology combining with spectral analysis method and supervised classification method was used to classify the farmland images. Since the hyperspectral data had the characteristic of huge and nonlinear, principal component analysis (PCA) and support vector machine (SVM) were adopted for classifier establishing. On the basis of spectral classification, spatial-spectral integration method was used to amend the spectral classification results, the isolated points and noise were removed. The results showed that the overall classification accuracy by SVM could reach 88.4%, and the highest overall classification accuracy by spatial-spectral integration method was up to 89.7%, indicating that using spatial information to modify spectral information could improve the classification accuracy of farmland objects, which would provide theoretical basis for hyperspectral image identification of near field farmland.

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备注/Memo

备注/Memo:
收稿日期:2017-10-31 基金项目:国家自然科学基金项目(31671571);山西农业大学青年科技创新基金项目(2017013) 作者简介:苗荣慧(1990-),女,山西晋城人,硕士,助教,主要从事图像分析与机器视觉、农产品无损检测研究。(Tel)18306828214;(E-mail)ronghui092@163.com
更新日期/Last Update: 2018-09-04