[1]刘瑶,李梓楠,吴涛,等.基于高光谱图像和邻域粗糙集理论的大豆品种识别算法及其综合性能评估[J].大豆科学,2018,37(04):596-605.[doi:10.11861/j.issn.1000-9841.2018.04.0596]
　LIU Yao,LI Zi-nan,WU Tao,et al.A Soybean Variety Identification Algorithm Based on Hyperspectral Image and Neighborhood Rough Set Theory and Its Comprehensive Performance Evaluation[J].Soybean Science,2018,37(04):596-605.[doi:10.11861/j.issn.1000-9841.2018.04.0596]
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基于高光谱图像和邻域粗糙集理论的大豆品种识别算法及其综合性能评估

《大豆科学》[ISSN:1000-9841/CN:23-1227/S] 卷: 第37卷 期数: 2018年04期 页码: 596-605 栏目: 出版日期: 2018-07-20
Title:
A Soybean Variety Identification Algorithm Based on Hyperspectral Image and Neighborhood Rough Set Theory and Its Comprehensive Performance Evaluation
作者:
刘瑶1; 李梓楠1; 吴涛1; 刘潋2; 孟祥丽1
（1.岭南师范学院 信息工程学院，广东 湛江 524048； 2.哈尔滨工程大学 信息与通信工程学院，黑龙江 哈尔滨 150001）
Author(s):
LIU Yao1;  LI Zi-nan1;  WU Tao1;  LIU Lian2;  MENG Xiang-li1
(1.School of Information Engineering, Lingnan Normal University, Zhanjiang 524048, China； 2.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
关键词:
大豆; 高光谱图像; 邻域粗糙集; 波段选择; 综合性能
Keywords:
Soybean;  Hyperspectral image;  Neighborhood rough set;  Band selection;  Comprehensive performance
DOI:
10.11861/j.issn.1000-9841.2018.04.0596
文献标志码:
A
摘要:
应用高光谱图像技术可以实现大豆品种的快速、高效、无损鉴别检测。高光谱图像数据量大、波段数量多，这导致数据传输、存储和处理有一定难度，故需应用波段选择方法进行数据降维。目前，面向品种识别的高光谱图像波段选择算法大多数都是以分类性能作为算法的评价标准，忽略了算法的稳定性。该文针对大豆品种识别问题，研究基于邻域粗糙集理论中的依赖度、一致性和信息熵等属性选择准则的高光谱波段选择算法，以Jaccard系数为稳定性度量指标，研究算法的稳定性随数据集扰动和子集大小的变化情况。针对波段选择算法的稳定性度量与分类模型之间是相互独立的这一问题，不能盲目追求高的稳定性而忽略特征子集的分类效果，提出当波段子集大小相同时采用Pareto最优解来评估算法综合性能；当波段子集大小不同时，采用兼顾分类性能、稳定性和子集大小的综合评价函数（PSN）评估算法性能。研究结果对获取综合性能最佳的波段子集有一定的理论及应用价值。
Abstract:
The rapid, efficient and nondestructive identification for soybeans varieties can be realized by using hyperspectral image technology. Hyperspectral image data is large and contains hundreds of bands, therefore it is difficult for data transmission, storage and processing. It is necessary to use band selection method for dimension reduction. At present, band selection algorithms of hyperspectral image for variety identification mainly take classification performance as evaluation criteria, and the stability of the algorithms is ignored. To solve the problem of variety identification for soybeans, hyperspectral band selection algorithms based on the dependence, consistency and information entropy criteria in neighborhood rough set theory were studied in this paper. By introducing Jaccard index as a metric of stability, the changes of stability of the algorithm with the dataset perturbation and the subset size were explored. Since the stability measurement and classification model of band selection algorithm are independent of each other, it couldn’t pursue high stability only and ignored the classification effect.To assess the comprehensive performance of algorithms, for the subsets with the same size, Pareto optimal solutions was proposed. For the subsets with different size, a comprehensive evaluation function of classification performance, stability and subset size was proposed. The results have certain theoretical and application value for obtaining the best band subset of comprehensive performance.

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备注/Memo

收稿日期：2018-03-05

基金项目：黑龙江省自然科学基金重点项目（ZD201303）；湛江市科技攻关计划项目（2017B01143）；岭南师范学院自然科学研究人才专项（ZL1902）。
第一作者简介：刘瑶（1982-），女，博士，讲师，主要从事高光谱图像处理技术研究。E-mail：liuyao@lingnan.edu.cn。
通讯作者：吴涛（1980-），男，博士，副教授，主要从事网络学习、智能信息处理研究。E-mail：wutao@whu.edu.cn。

更新日期/Last Update: 2018-08-01