Please wait a minute...
img

官方微信

遥感技术与应用  2015, Vol. 30 Issue (6): 1095-1102    DOI: 10.11873/j.issn.1004-0323.2015.6.1095
模型与反演     
基于高光谱非线性向量空间的光谱曲线特征差异性分析
戴晓爱,杨晓霞,高孝杰,杨武年,贾虎军,杨叶,潘佩芬 
 (成都理工大学地学空间信息技术国土资源部重点实验室/地球科学学院,四川 成都610059)
The Difference Analysis of Spectral Curve Features on Hyperspectral Nonlinear Vector Space
Dai Xiaoai,Yang Xiaoxia,Gao Xiaojie,Yang Wunian,Jia Hujun,Yang Ye,Pan Peifen
 (Chengdu University of Technology State Key Laboratory of Geo-spatial Information Technology
Ministry of Land and Resources/ College Of Earth Sciences,Chengdu 610059,China)
 全文: PDF(2752 KB)  
摘要:

地物光谱特征分析是对地物进行分类和匹配的基础,目前高光谱遥感技术应用在精细物种识别中主要采用波谱分析的方法。重点探索非线性空间里的相似性测度方法,由于光谱曲线表征复杂光谱成像的非线性过程,论文从空间目标的整体形状描述非空集合之间的差异,采用Fr′echet距离、Hausdorff 距离、Euclidian距离分别定义光谱特征曲线的距离,设计算法测量光谱向量之间的非线性相似程度。结果表明,采用Fr′echet距离、Hausdorff 距离、Euclidian距离度量光谱相似度的精度依次减弱,但依据Fr′echet距离的算法时间复杂度略高。基于Fréchet距离的方法充分考虑了曲线上点的位置信息及整体曲线的走势问题,其在精度、抗噪能力等方面均有提升,从而为分析光谱特征提供了可能的新途径。

关键词: 光谱相似度高光谱光谱曲线特征非线性向量空间    
Abstract:

It is the basis of classifying feature and matching that the characteristics of spectral analysis.Now the spectrum analysis is the main way in the fine species identification using the hyperspectral remote sensing technology.This paper focuses on exploring the nonlinear space in the inner similarity measure.Since the spectral curve of characterization can express the complex nonlinear spectral imaging process.The paper describes the non-empty set from the overall shape of space target.The distance between spectrum characteristic curve was defined Fr ′ echet distance,Hausdorff distance and Euclidean distance respectively and algorithm was designed to measure nonlinear similar degree between the spectral vector.The results show that the accuracy about Fr ′ echet distance,Hausdorff distance,the precision of the Euclidean distance to measure the spectral similarity is once weakened,but on the basis of Fr′echet distance algorithm time complexity is a little high.This method based on Fr′echet distance improves the accuracy and denoising performance,due to considering point position and trend of the curve,thereby which provides an effective tool to spectral analysis.

Key words: Spectral similarity    Hyperspectral    Spectral curve features    Nonlinear vector space
收稿日期: 2014-05-08 出版日期: 2016-01-25
:  P 237  
基金资助:

国家自然科学基金项目“任务感知的遥感信息服务动动态组合方法”(41201440),“岷江上游毛儿盖地区生态水遥感量化研究”(41071265)、“汶川强震区潜在泥石流危险性判识及其差异性分析”(41102225),高等学校博士学科点专项科研基金项目“岷江上游高原林区不同植被类型的土壤持水特征研究”(201351221200092013),四川省教育厅科研项目“基于光谱相似度的森林树种识别方法研究——以青城山地区为例”(15ZB0066),成都理工大学研究基金项目“基于混合像元分解的岷江上游植被覆盖度定量估算研究”(2012YG02),国土资源部地学空间信息技术重点实验室课题(KLGSIT2013-02),成都理工大学中青年骨干教师培养计划(DG0002)共同资助。

通讯作者: 杨晓霞(1977-),女,山西太原人,博士,讲师,主要从事遥感与GIS研究。Email:Yangxx2003@126.com。    
作者简介: 戴晓爱(1979-),女,甘肃敦煌人,博士,副教授,主要从事遥感与GIS研究。Email:daixiaoa@cdut.cn。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
戴晓爱
杨晓霞
高孝杰
杨武年
贾虎军
杨叶
潘佩芬

引用本文:

戴晓爱,杨晓霞,高孝杰,杨武年,贾虎军,杨叶,潘佩芬 . 基于高光谱非线性向量空间的光谱曲线特征差异性分析[J]. 遥感技术与应用, 2015, 30(6): 1095-1102.

Dai Xiaoai,Yang Xiaoxia,Gao Xiaojie,Yang Wunian,Jia Hujun,Yang Ye,Pan Peifen. The Difference Analysis of Spectral Curve Features on Hyperspectral Nonlinear Vector Space. Remote Sensing Technology and Application, 2015, 30(6): 1095-1102.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.6.1095        http://www.rsta.ac.cn/CN/Y2015/V30/I6/1095

[1]Lin Chuan,Gong Zhaoning,Zhao Wenji,et al.Identifying Typical Plant Ecological Types based on Spectral Characteristic Variables:A Case Study in Wild Duck Lakewetland,Beijing[J].Acta Ecologica Sinica,2013,33(4) :1172-1185.[林川,宫兆宁,赵文吉,等.基于光谱特征变量的湿地典型植物生态类型识别方法-以北京野鸭湖湿地为例[J].生态学报,2013,33(4):1172-1185.]

[2]Ghiyamat A,Helmi Z S,Mahdiraji G A,et al.Hyperspectral Tiscrimination of Tree Species with Different Classification Using Single-and Multiple-endmenber[J].International Journal of Applied Earth Observation and Geoinformation,2013,23:177-191.

[3]Liu Yingying.Surrmary for Indentification Method of Moneral Using Hyperspectral Remote Sensing[J].Beijing Surveying and Mapping,2012,(6):6-10,21.[刘莹莹.高光谱遥感岩矿识别方法的研究进展[J].北京测绘,2012,(6):6-10,21.]

[4]Yan Shouxun,Zhang Bing,Zhao Yongchao,et al.Summarizing the Technical Flow and Main Approaches for Discrimination and Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing[J].Remote Sensing Technology and Application,2004,19(1):52-63.[燕守勋,张兵,赵永超,等.高光谱遥感岩矿识别填图的技术流程与主要技术方法综述[J].遥感技术与应用,2004,19(1):52-63.]

[5]Huang Wei.Review of Hyperspectral Remote Sensing Classification and Information Extraction[J].Digital Technology & Application,2010,(5):134-136.[黄玮.高光谱遥感分类与信息提取综述[J].数字技术与应用,2010,(5):134-136.]

[6]Bao Gang,Bao Yuhai,Qin Zhihao,et al.Hyper-spectral Remote Sensing Estimation for the Vegetation Cover[J].Journal of Natural Resources,2013,28(7):1243-1254.[包刚,包玉海,覃志豪.高光谱植被覆盖度遥感估算研究[J].自然资源学报.2013,28(7):1243-1254.]

[7]Wang Zhihui,Ding Lixia.Tree Species Discrimination based on Leaf-Level Hyperspectral Characteristic Analysis[J].Spectroscopy and Spectral Analysis,2010,30(7):1825-1829.[王志辉,丁丽霞.基于叶片高光谱特性分析的树种识别[J].光谱学与光谱分析,2010,30(7):1825-1829.]

[8]Tong Qingxi,Zhang Bing,Zheng Lanfen.Hyperspectral Remote Sensing Principle,Technology and Application[M].Beijing:Higher Education Press,2006.[童庆禧,张兵,郑兰芬.高光谱遥感原理、技术与应用[M].北京:高等教育出版社,2006.]

[9]Du Peijun,Tang Hong,Fang Tao.Algorithms for Spectral Similarity Measure in Hyperspectral Remote Sensing[J].Geomatics and Information Science of Wuhan University,2006,31(2):112-115.[杜培军,唐宏,方涛.高光谱遥感光谱相似性度量算法与若干新方法研究[J].武汉大学学报(信息科学版),2006,31(2):112-115.]

[10]Chen Wei,Yu Xuchu,Zhang Gang,et al.A Novel Similiarity Measurement based on Object Detection in Hyperspectral Imagery[J].Journal of Geomatics Science and Technology,2012,(1):42-46.[陈伟,余旭初,张钢,等.基于新型相似性测度的高光谱影像地物检测[J].测绘科学技术学报,2012,(1):42-46.]

[11]Wen Binggong,Feng Wufa,Liu Wei.Matching and Classification based on the Whole Comparability Measure of Spectral Curve[J].Journal of Geometrics Science and Technology,2009,26(2):128-131.[闻兵工,冯伍法,刘伟.基于光谱曲线整体相似性测度的匹配分类[J].测绘科学技术学报,2009,26(2):128-131.]

[12]Shi Beiqi,Liu Chun,Chen Neng,et al.Spectral Similarity Measure and Experimental Analyses for Field Spectroscopy[J].Journal of Tongji University(Natural Science),2011,39(2):292-298.[施蓓琦,刘春,陈能,等.典型地物实测光谱的相似性测度与实验分析[J].同济大学学报(自然科学版),2011,39(2):292-298.]

[13]Cheng Xi,Shen Zhanfeng,Zhou Yanan,et al.The Spectral Characteristics Separability Analysis of Spectral Database of Typical Objects of Land Surface based on Bhattachryya Distance[J].Remote Sensing Technology and Applicaton,2013,28(4):707-713.[程熙,沈占锋,周亚男,等.基于Bhattacharyya距离的典型地物波谱特征差异性分析[J].遥感技术与应用,2013,28(4):707-713.]

[14]Zhang J,Koo I,Wang B,et al.A Large Scale Test Dataset to Determine Optimal Retention Index Threshold based on Three Mass Spectral Similarity Measures[J].Journal of Chromatography A,2012,1251:188-193.

[15]Fu X,Kim M S,Chao K,et al.Detection of Melamine in Milk Powders based on NIR Hyperspectral Imaging and Spectral Similarity Analyses[J].Journal of Food Engineering,2014,124:97-104.

[16]Van der Meer F.The Effectiveness of Spectral Similarity Me-asures for the Analysis of Hyperspectral Imagery[J].International Journal of Applied Earth Observation and Geoinformation.2006,8(1):3-17.

[17]Xuan Guorong.An Euclidean Distance Feature Selection Me-thod in Pattern Recognition[J].Computer Applications and Software,1985,6:5-12.[宣国荣.模式识别中欧氏距离特征选择新方法[J].计算机应用与软件,1985,6:5-12.]

[18]Mémoli F.Some Properties of Gromov-Hausdorff Distances[J].Discrete & Computational Geometry,2012,48(2):416-440.

[19]Pelletier S.Computing the Fréchet Distance between Two Polygonal Curves:Computational Geometry 2002.Available from:http://www.cim.mcgill.ca/~stephane/cs507/Project.html.

[20]Helmut A,Michel G.Computing the Fréchet Distance between Two Polygonal Curves[J].International Journal of Computational Geometry & Applications,1993,5(1-2):75-91.

[1] 陈伟民,张凌,宋冬梅,王斌,丁亚雄,许明明,崔建勇. 基于AdaBoost改进随机森林的高光谱图像地物分类方法研究[J]. 遥感技术与应用, 2018, 33(4): 612-620.
[2] 苏阳,祁元,王建华,徐菲楠,张金龙. 基于航空高光谱影像的额济纳绿洲土地覆被提取[J]. 遥感技术与应用, 2018, 33(2): 202-211.
[3] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.
[4] 郭宇柏,卓莉,陶海燕,曹晶晶,王芳. 基于空谱初始化的非负矩阵光谱混合像元盲分解[J]. 遥感技术与应用, 2018, 33(2): 216-226.
[5] 刘爱林,郭宝平,李岩山 . 基于离散粒子群算法的凸多模态高光谱图像端元提取研究[J]. 遥感技术与应用, 2018, 33(2): 227-232.
[6] 吴兴,张霞,孙雪剑,张立福,戚文超. SPARK卫星高光谱数据辐射质量评价[J]. 遥感技术与应用, 2018, 33(2): 233-240.
[7] 宋婷婷,付秀丽,陈玉,魏永明,王钦军,程先锋. 云南个旧矿区土壤锌污染遥感反演研究[J]. 遥感技术与应用, 2018, 33(1): 88-95.
[8] 刘慧珺,苏红军,赵-波. 基于改进萤火虫算法的高光谱遥感多特征优化方法[J]. 遥感技术与应用, 2018, 33(1): 110-118.
[9] 李伟娜,韦玮,张怀清,刘华. 基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J]. 遥感技术与应用, 2017, 32(5): 809-817.
[10] 肖昊,王杰. 基于IDL和MATLAB混合编程的两种光谱混合分析方法比较[J]. 遥感技术与应用, 2017, 32(5): 858-865.
[11] 唐超,邵龙义. 高光谱遥感地物目标识别算法及其在岩性特征提取中的应用[J]. 遥感技术与应用, 2017, 32(4): 691-697.
[12] 李恒凯,欧彬,刘雨婷,邱玉宝. 基于混合像元分解的高光谱影像柑橘识别方法[J]. 遥感技术与应用, 2017, 32(4): 743-750.
[13] 苏红军,赵波. 基于共形几何代数的高光谱遥感波段选择方法[J]. 遥感技术与应用, 2017, 32(3): 539-545.
[14] 史飞飞,高小红,杨灵玉,何林华,贾伟. 基于HJ-1A高光谱遥感数据的湟水流域典型农作物分类研究[J]. 遥感技术与应用, 2017, 32(2): 206-217.
[15] 李焱,王让会,管延龙,蒋烨林,吴晓全,彭擎. 基于高光谱反射特性的土壤全氮含量预测分析[J]. 遥感技术与应用, 2017, 32(1): 173-179.