我们的网站为什么显示成这样?

可能因为您的浏览器不支持样式,您可以更新您的浏览器到最新版本,以获取对此功能的支持,访问下面的网站,获取关于浏览器的信息:

|Table of Contents|

基于小波分析的Hyperion影像地物分类波段宽度(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:
141-147
Column:
研究论文
publishdate:
2018-09-15

Article Info:/Info

Title:
Suitability of band width for Hyperion image classification based on wavelet analysis
Article ID:
1000-2006(2018)05-0141-07
Author(s):
FANG Xiufeng1 TAN Bingxiang1* DU Huaqiang2 WANG Huaijing1 LI Taixing3
1. Institution of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China; 2. School of Enviromental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China; 3. Baihe Forestry Bureau of Jilin Provinc
Keywords:
Hyperion image wavelet decomposition wavelet fusion classification and identification of forest
Classification number :
S758; TP79
DOI:
10.3969/j.issn.1000-2006.201710042
Document Code:
A
Abstract:
【Objective】Hyperspectral remote sensing is advantageous for fine identification of features, but has the problem of large data volume, high correlations among bands, and low processing accuracy and efficiency. In remote sensing classification, it is not the case that the use of a greater number of narrow bands ensures better classification perfor-mance. Therefore, the primary objective of the current work was to determine an appropriate bandwidth of Hyperion images by exploring the reduction of hyperspectral data, from the perspective of spectra.【Method】In this study, the preprocessing procedures for Hyperion images principally included the removal of uncalibrated and water vapor affected bands, eliminating the bad lines, stripes and smile effects, radiometric calibration and atmospheric correction. After this processing, 161 bands were retained to implement further geometric correction. Fourteen land use classes or schemes in the experimental area were selected according to field sample plot inventories, and a 7-scale Sym 3 wavelet decomposition was carried out on the average spectra of the 14 land use types. The spectral bands suitable for each type of recognition were mainly concentrated around 40 and 80 nm because of the variance and entropy of the wavelet detail coefficients. The narrow bands identified in the different spectral intervals were fused using the wavelet function to classify, by implementation, the support vector machines(SVM)algorithm.【Result】It was concluded that the appropriate spectral resolution to identify nine types of ground objects, such as Pinus syluestriformis forest, Larix gmelinii forest, Pinus sylvestris forest, mixed conifers forest, mixed broadleaf forest, fire burned area, water, arable land, and unused land, is 40 nm and the suitable spectral resolution for the remaining five kinds of terrain recognitions is 80 nm. The number of bands in different spectral intervals was greatly reduced. Further, the final classification accuracy was over 81%. 【Conclusion】The combination of wavelet analysis and SVM was helpful to solve the “dimensional disaster” issue with hyperspectral data, which indicates that the proposed method is feasible.

References

[1] 杜培军, 夏俊士, 薛朝辉,等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2016, 20(2):236-256. DOI: 10.11834/jrs.20165022.
DU P J, XIA J S, XUE Z H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256.
[2] 李显彬, 姜小光, 刘亮,等. 基于光谱重建的高光谱特征参数选择方法——以苏北地区Hyperion数据为例[J]. 遥感学报, 2007, 11(4):589-594.DOI: 10.3321/ j.issn:1007-4619.2007.04.022.
LI X B, JIANG X G, LIU L, et al. A new feature selection method for EO-1 Hyperion image classification: a case study of Subei region, China[J]. Journal of Remote Sensing, 2007, 11(4):589-594.
[3] 林志垒, 晏路明. 高光谱影像的M-ICA地物识别算法与应用[J]. 地球信息科学学报, 2011, 13(1):126-132.DOI:10.3724/SP.J.1047.2011.00126.
LIN Z L, YAN L M. Object recognition algorithm and its application on hyperspectral imagery based on M-ICA[J]. Geo-information Science,2011,13(1):126-132.
[4] LUCAS R, BUNTING P, PATERSON M, et al. Classification of Australian forest communities using aerial photography, CASI and HyMap data[J]. Remote Sensing of Environment, 2008, 112(5):2088-2103.DOI:10.1016/j.rse.2007.10.011.
[5] WARNER T A, SHANK M C. Spatial autocorrelation analysis of hyperspectral imagery for feature selection [J]. Remote Sensing of Environment, 1997, 60(1):58-70.DOI:10.1016/S0034-4257(96)00138-1.
[6] 高恒振. 高光谱遥感图像分类技术研究[D]. 北京:国防科学技术大学,2011.
GAO H Z. Rasearch on classification techinique for hyperspectral remote sensing imagery[D]. Beijing: National University of Defense Technology,2011.
[7] 柳萍萍, 林辉, 孙华,等. 高光谱数据的降维处理方法研究[J].中南林业科技大学学报,2011, 31(11):34-38. DOI: 10.3969/j.issn.1673-923X.2011.11.007.
LIU P P, LIN H, SUN H, et al. Dimensionality reduction method of hyperion EO-1 data[J]. Journal of Central South University of Forestry and Technology, 2011, 31(11):34-38.
[8] 陈刚, 陈小梅, 李婷,等. 基于小波分解的光谱特征提取算法研究[J]. 光谱学与光谱分析, 2010, 30(11):3027-3030. DOI:10.3964/j.issn.1000-0593(2010)11-3027-04.
CHEN G, CHEM X M, LI T, et al. Research on spectral data feature extraction based on wavelet decomposition[J]. Spectroscopy and Spectral Analysis, 2010, 30(11):3027-3030.
[9] KAEWPIJIT S, MOIGNE J L, El-GHAZAWI T. Automatic reduction of hyperspectral imagery using wavelet spectral analysis[J]. IEEE Transactions on Geoscience & Remote Sensing, 2003, 41(4):863-871. DOI:10.1109/TGRS.2003.810712.
[10] 刘燕德, 欧阳爱国, 应义斌. 小波分析用于光谱信号处理及其在Matlab中的实现[J]. 传感技术学报, 2006, 19(3):821-823. DOI: 10.3969/j.issn.1004-1699. 2006.03.071.
LIU Y D, OUYANG A G, YING Y B. Application of wavelet analysis in signal process using matlab[J]. Chinese Journal of Senior and Actuators, 2006, 19(3):821-823.
[11] GONZALEZ-AUDICANA M, SALETA J L, CATALAN R G, et al. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition[J]. Geoscience & Remote Sensing IEEE Transactions on, 2004, 42(6):1291-1299.DOI:10.1109/TGRS.2004.825593.
[12] 谭炳香, 李增元, 陈尔学, 等. EO-1 Hyperion 高光谱数据的预处理[J].遥感信息, 2005(6):36-41. DOI:10.3969/j.issn.1000-3177.2005.06.010.
TAN B X, LI Z Y, CHEN E X, et al. Preprocessing of EO-1 Hyperion hyperspectral data[J]. Remote Sensing Information, 2005(6):36-41.
[13] 梁继, 王建. Hyperion高光谱影像的分析与处理[J]. 冰川冻土, 2009, 31(2):63-69.
LIANG J, WANG J. Hyperion hyperspectral image: analysis and process[J]. Journal of Glaciology and Geocryology, 2009, 31(2):63-69.
[14] ZHU R H, WAN M, FAN G B. An image fuse method based on pyramid transformation[J]. Computer Simulation, 2007, 24(12):178-181.
[15] 郭志强. 基于区域特征的小波变换图像融合方法[J]. 武汉理工大学学报, 2005, 27(2):65-67. DOI: 10.3321/j.issn:1671-4431.2005.02.020.
GUO Z Q. Wavelettransform image fusion based on regional features[J].Journal of Wuhan University of Technology, 2005, 27(2):65-67.
[16] 桑琦, 陈浩, 李卫华. 基于小波变换的图像融合方法仿真[J]. 电子科技, 2017, 30(3):1-3. DOI: 10.16180/j.cnki.issn1007-7820.2017.03.001.
SANG Q, CHEN H, LI W H. Image fusion method simulation based on wavelet transform[J]. Electronic Science and Technology, 2017, 30(3):1-3.
[17] ROSSO O A, BLANCO S, YORDANOVA J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals[J]. Journal of Neuroscience Methods, 2001, 105(1):65-75. DOI: 10.1016/S0165-0270(00)00356-3.
[18] 陈玉明, 吴克寿, 李向军. 一种基于信息熵的异常数据挖掘算法[J]. 控制与决策, 2013(6):867-872.
CHEN Y M, WU K S, LI X J. A kind of outlier mining algorithm based on information entropy[J]. Control and Decision, 2013(6):867-872.
[19] 浦瑞良, 宫鹏. 高光谱遥感及其应用[M]. 北京:高等教育出版社, 2000.

Last Update: 2018-09-15