[1]刘丹凤,王立国,赵亮.高光谱图像的同步彩色动态显示[J].哈尔滨工程大学学报,2014,(06):760-765.[doi:10.3969/j.issn.10067043.201306008]
 LIU Danfeng,WANG Liguo,ZHAO Liang.Dynamic display of the hyperspectral image synchronized colors[J].hebgcdxxb,2014,(06):760-765.[doi:10.3969/j.issn.10067043.201306008]
点击复制

高光谱图像的同步彩色动态显示(/HTML)
分享到:

《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年06期
页码:
760-765
栏目:
出版日期:
2014-06-25

文章信息/Info

Title:
Dynamic display of the hyperspectral image synchronized colors
文章编号:
10067043(2014)06076006
作者:
刘丹凤 王立国 赵亮
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 Danfeng WANG Liguo ZHAO Liang
College of Information and Communication Engineering,Harbin Engineering University, Harbin 150001, China
关键词:
遥感高光谱图像可视化颜色匹配方程彩色视觉动态显示
分类号:
TN911.73
DOI:
10.3969/j.issn.10067043.201306008
文献标志码:
A
摘要:
高光谱数据的CMF加权封装方法是目前由高光谱数据获取近真彩色图像的主要方法,同时也因其计算简便而受到广泛关注。针对CMF图像分辨率较低问题进行改进,加入时间维,采用位移循环加权方法,变静止图像为动态图像,建立了一种适用于高光谱图像的同步彩色动态显示模型,使观察者在相同观察时间内获得尽可能多的信息。实验表明,生成的图像不仅能够产生接近于真彩色的图像,而且使得不同地物呈现不同色彩和不同的色彩变化率,增强了视觉感官效果。同时该方法也满足计算简便这一设计目标,适用于对高光谱数据进行实时性观察。

参考文献/References:

[1]BEAUCHEMIN M, FUNG K B. On statistical band selection for image visualization[J]. Photogrammetric Engineering and Remote Sensing, 2001, 67(5): 571574. [2]ROBERTSON P K, O’ CALLAHAN J F. The application of perceptual color spaces to the display of remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 26(1): 4959. [3]JACOBSON N P, GUPTA M R. Design goals and solutions for display of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(11): 26842693. [4]TYO J S, KONSOLAKIS A, DIERSEN D I, et al. Principalcomponentsbased display strategy for spectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(3): 708718. [5]CAI S, DU Q, MOORHEAD R, et al. Noiseadjusted principal component analysis for hyperspectral remotely sensed imagery visualization[C]//Proceedings of IEEE Visualization 2005 (VIS 2005).[S.l.], 2005: 119120.[6]GUO Baofeng, GUNN S, DAMPER B, et al. Hyperspectral image fusion using spectrally weighted kernels[C]//2005 8th International Conference on Information Fusion.[S.l.], 2005: 2528. [7]WILSON T A, ROGERS S K, KABRISKY M. Perceptualbased image fusion for hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4): 10071017. [8]KOTWAL K, CHAUDHURI S. Visualization of hyperspectral images using bilateral filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5): 23082316.  [9]CUI Ming, RAZDAN A, HU Jiuxiang, et al. Interactive hyperspectral image visualization using convex optimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6):16731684. [10]MIGNOTTE M. A multiresolution Markovian fusion model for the color visualization of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 42364247. [11]MIGNOTTE M. A Bicriteriaoptimizationapproachbased dimensionalityreduction model for the color display of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2): 501513. [12]JIMENEZ L O, LANDGREBE D A. Supervised classification in highdimensional space: geometrical, statistical, and asymptotical properties of multivariate data[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 1998, 1: 954. [13]Le MOAN S, MANSOURI A, VOISIN Y, et al. A constrained band selection method based on information measures for spectral image color visualization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(12): 51045115. [14]DEMIR B, CELEBI A, ERTURK S. A lowcomplexity approach for the color display of hyperspectral remotesensing images using onebittransformbased band selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 97105. [15]LEE J H, HEO A, CHOI W C, et al. Visualization of hyperspectral images using bilateral filtering with spectral angles[C]//Proceedings of SPIE Signal Processing Sensor Fusion and Target Recognition.[S.l.], 2011, 4: 80501X180501X6.

相似文献/References:

[1]赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2010,(11):0.
 College of Information and Communication Engineering,Harbin Engineering University,Harbin,et al.Anomaly detection algorithm for hyperspectral image by using selective section principal component analysis[J].hebgcdxxb,2010,(06):0.
[2]赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2011,(01):109.[doi:doi:10.3969/j.issn.1006-7043.2011.01.020]
 ZHAO Chunhui,HU Chunmei,SHI Hong.Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm[J].hebgcdxxb,2011,(06):109.[doi:doi:10.3969/j.issn.1006-7043.2011.01.020]
[3]刘振林,谷延锋,张晔.一种用于高光谱图像特征提取的子空间核方法[J].哈尔滨工程大学学报,2014,(02):238.[doi:10.3969/j.issn.10067043.201309025]
 LIU Zhenlin,GU Yanfeng,ZHANG Ye.A subspace kernel learning method for feature extraction of the hyperspectral image[J].hebgcdxxb,2014,(06):238.[doi:10.3969/j.issn.10067043.201309025]
[4]杨京辉,王立国,钱晋希.基于相关向量机的高光谱图像解混方法[J].哈尔滨工程大学学报,2015,(02):267.[doi:10.3969/j.issn.1006-7043.201311016]
 YANG Jinghui,WANG Liguo,QIAN Jinxi.An unmixing algorithm based on the relevance vector machine for hyperspectral imagery[J].hebgcdxxb,2015,(06):267.[doi:10.3969/j.issn.1006-7043.201311016]
[5]赵春晖,靖晓昊,李威.基于StOMP稀疏方法的高光谱图像目标检测[J].哈尔滨工程大学学报,2015,(07):992.[doi:10.3969/j.issn.1006-7043.201404087]
 ZHAO Chunhui,JING Xiaohao,LI Wei.Hyperspectral image target detection algorithm based on StOMP sparse representation[J].hebgcdxxb,2015,(06):992.[doi:10.3969/j.issn.1006-7043.201404087]
[6]赵春晖,王佳,王玉磊.采用背景抑制和自适应阈值分割的高光谱异常目标检测[J].哈尔滨工程大学学报,2016,37(02):278.[doi:10.11990/jheu.201409035]
 ZHAO Chunhui,WANG Jia,WANG Yulei.Hyperspectral anomaly detection based on background suppression and adaptive threshold segmentation[J].hebgcdxxb,2016,37(06):278.[doi:10.11990/jheu.201409035]
[7]王立国,宛宇美,路婷婷,等.结合经验模态分解和Gabor滤波的高光谱图像分类[J].哈尔滨工程大学学报,2016,37(02):284.[doi:10.11990/jheu.201411032]
 WANG Liguo,WAN Yumei,LU Tingting,et al.Hyperspectral image classification by combining empirical mode decomposition with Gabor filtering[J].hebgcdxxb,2016,37(06):284.[doi:10.11990/jheu.201411032]
[8]王立国,杨月霜,刘丹凤.基于改进三重训练算法的高光谱图像半监督分类[J].哈尔滨工程大学学报,2016,37(06):849.[doi:10.11990/jheu.201505078]
 WANG Liguo,YANG Yueshuang,LIU Danfeng.Semi-supervised classification for hyperspectral image based on improved tri-training method[J].hebgcdxxb,2016,37(06):849.[doi:10.11990/jheu.201505078]
[9]成宝芝,赵春晖,张丽丽.子空间稀疏表示高光谱异常检测新算法[J].哈尔滨工程大学学报,2017,38(04):640.[doi:10.11990/jheu.201604006]
 CHENG Baozhi,ZHAO Chunhui,ZHANG Lili.An anomaly detection algorithm for hyperspectral images using subspace sparse representation[J].hebgcdxxb,2017,38(06):640.[doi:10.11990/jheu.201604006]
[10]盛振国,王立国.改进的LLGC高光谱图像半监督分类[J].哈尔滨工程大学学报,2017,38(07):1086.[doi:10.11990/jheu.201605023]
 SHENG Zhenguo,WANG Liguo.Semi-supervised classification for hyperspectral images based on improved learning with the LLGC method[J].hebgcdxxb,2017,38(06):1086.[doi:10.11990/jheu.201605023]
[11]赵春晖,王鑫鹏,闫奕名.基于密度背景纯化的高光谱异常检测算法[J].哈尔滨工程大学学报,2016,37(12):1722.[doi:10.11990/jheu.201511073]
 ZHAO Chunhui,WANG Xinpeng,YAN Yiming.Density background refinement-based anomaly detection algorithm for hyperspectral images[J].hebgcdxxb,2016,37(06):1722.[doi:10.11990/jheu.201511073]

备注/Memo

备注/Memo:
收稿日期: 2013-06-03. 网络出版时间:2014-05-14 15:50:25. 基金项目:国家自然科学基金资助项目(61275010);黑龙江省自然科 学基金重点资助项目(ZD201216). 作者简介:刘丹凤(1987-),女,博士研究生; 王立国(1974-),男,教授,博士生导师. 通信作者:刘丹凤,E-mail: liudanfeng@ hrbeu.edu.cn.
更新日期/Last Update: 2014-09-01