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|Table of Contents|

基于主成分变换的多光谱遥感数据的有监督可视化方法(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2018年05期
Page:
121-128
Column:
研究论文
publishdate:
2018-09-15

Article Info:/Info

Title:
Supervised visualization of multispectral remotely sensed data based on principal component transformation
Article ID:
1000-2006(2018)05-0121-08
Author(s):
ZHANG Jianchen CUI Jie CHENG Yi HUANG Jian DU Jingyuan GE Hongli*
School of Environmental & Resource Sciences, Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Lin'an 311300,China
Keywords:
multispectral remote sensing data supervised visualization visual interpretation principal component transformation(PCT) regression reconstruction of principal component transform coefficients(RRPCTC) forest resource monitoring
Classification number :
TP751; S758
DOI:
10.3969/j.issn.1000-2006.201704017
Document Code:
A
Abstract:
【Objective】Visualization of remotely sensed data is a key step to determine the interpretation quality of remotely sensed data. Visual interpretation is an important way of applying remotely sensed technology in current production practice. To exploring a method for generating visual features which based on learning samples, so that visual and visual interpreting applications can be organically combined to achieve supervised visualization. 【Method】Using the newly proposed regression reconstruction of principal component transform coefficients(RRPCTC)to constructed the model,three new features are obtained,which are respectively assigned red,green and blue three colors in order to generate false color images for visualization.【Result】The image produced by RRPCTC is improved to a certain extent compares with the ordinary principal component transformation(OPCT)method and the original near infrared-short wave infrared-red 3-band combination. It is helpful for visual interpretation. The classification accuracy of RRPCTC is improved by 6.20% compared with that of the OPCT and by 7.82% compared with that of the original 3-band combination. The changes of sums of squares of deviations of the first 3 PCs respectively and the deviation matrix before and later reconstruction show that the reconstruction makes the differences smaller inner classes and larger between classes.【Conclusion】All these quantitatively prove that the separability of the RRPCTC is better than that of OPCT and that of original 3-band combination.

References

[1] 钱乐祥. 遥感数字影像处理与地理特征生成 [M]. 北京:科学出版社, 2004.
QIAN L X. Remote sensing digital image processing and geographical feature generation[M]. Beijing: Science Press, 2004.
[2] 苏红军, 盛业华, 杜培军. 自动子空间划分在高光谱影像波段选择中的应用[J].地球信息科学学报, 2007, 9(4): 123-128.
SU H J, SHENG Y H, DU P J. Application of automatic subspace partitioning in hyperspectral image band selection[J]. Journal of Earth Information Science, 2007, 9(4): 123-128.
[3] STEFANO B, ANDREA E,FABRIZIO S. Feature selection for ordinal text classification[J]. Neural Computation, 2014,26(3):557-591.
[4] 孙华,鞠洪波,张怀清,等. Hyperion高光谱影像波段选择方法比较研究[J]. 红外, 2013, 34(2): 27-34.
SUN H,JU H B,ZHANG H Q,et al. Comparative study of band selection methods for hyperspectral imagery Hyperion[J]. Infra-red,2013, 34(2): 27-34.
[5] CHANG C I, DU Q, SUN T L, et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification [J]. IEEE Transactions on Geoscience & Remote Sensing, 1999, 37(6): 2631-2641.
[6] LI W, PRASAD S, FOWLER J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis [J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(4): 1185-1198.
[7] 刘翔.基于光谱维变换的高光谱图像目标检测研究[D].北京:中国科学院遥感应用研究所,2008.
LIU X. Research on hyperspectral image target detection bases on spectral dimension transform[D]. Beijing: Institute of Remote Sensing Applications, Chinese Academy of Sciences,2008.
[8] GREENA A, BERMAN M, SWITZER P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J]. IEEE Transactions on Geoscience & Remote Sensing,1988,26(1):65-74.
[9] FRITZ R, FRECH I, KOCH B, et al.Sensor fused images for visual interpretation of forest stand borders [J]. International Archives of Photogrammetry and Remote Sensing, 1999(32):7-13.
[10] 林辉, 熊育久, 孙华, 等. 湖南省森林资源连续清查遥感应用研究 [J]. 中南林业科技大学学报, 2007, 27(4): 33-38.DOI:10.14067/J.CNKI.1673-923x.2007.04.020.
LIN H, XIONG Y J, SUN H, et al. Institute of remote sensing applications of continuous forest inventory in Hunan Province[J]. Journal of Central South University of Forestry and Technology,2007,27(4):33-38.
[11] 许筱阳, 李元科, 赵鹏祥. TM影像森林类型解译中若干问题的探讨 [J]. 西北林学院学报, 1993(1): 100-104.
XU X Y, LI Y K, ZHAO P X. Discussion on some problems in interpretation of forest types by TM images[J]. Journal of Northwest Forestry College,1993(1):100-104.
[12] 王晓晶, 王蓉, 郑团结, 等. 面向森林资源调查应用的天绘一号数据影像融合算法评价分析 [J]. 林业资源管理, 2013(3): 138-142.DOI:10.13466/j.cnki.lyzygl.2013.03.005.
WANG X J, WANG R, ZHENG T J, et al. Sky painting NO. 1 for forest resources investigation and application for evaluation and analysis of data fusion algorithms for forest resources survey[J]. Forestry Resource Management,2013(3):138-142.
[13] 熊立伟, 吴湘滨, 谭红伟. 遥感影像元数据多维可视化方法的设计与实现[J]. 遥感信息, 2016,31(2):60-63.DOI:10.3969/j.issn.1000-3177.2016.02.011.
XIONG L W, WU X B, TAN H W. Design and implementation of remote sensing image metadata multidimensional visualization method[J]. Remote Sensing Information,2016,31(2):60-63.
[14] 修珍珍,王斌,杨校生,等.庙山坞自然保护区森林生态系统服务功能评价[J]. 南京林业大学学报(自然科学版), 2015,39(4):81-87.DOI:10.3969/j.issn.1000-2006.2017.01.001.
XIU Z Z, WANG B, YANG X S,et al. Evaluation on service function of forest ecosystem in Miaoshanwu Nature Reserve[J]. Journal of Nanjing Forestry University(Natural Sciences Edition),2015,39(4):81-87.
[15] 李盛阳, 于海军, 韩洁, 等. 基于三维地球的海量遥感影像高效可视化管理系统的设计与实现[J]. 遥感技术与应用, 2016,31(1):170-176.DOI:10.11873/j.issn.1004-0323.2016.1.0170.
LI S Y, YU H J, HAN J, et al. Design and implementation of efficient visualization management system of massive remote sensing images based on 3D earth[J]. Remote Technology and Application,2016,31(1):170-176.
[16] PEARSON K. On lines and planes of closest fit to systems of points in space[J]. Philosophical Magazine,1901,2(11):559-572.
[17] HOTELLING H. Analysis of a complex of statistical variables into principal components [J]. British Journal of Educational Psychology, 1932, 24(6): 417-520.
[18] 朱建平. 应用多元统计分析[M]. 北京:科学出版社,2012:34-37.

Last Update: 2018-09-15