Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2011, Vol. 23 Issue (1) : 77-82     DOI: 10.6046/gtzyyg.2011.01.15
Technology and Methodology |
Research on Standard Preprocessing Flow for HJ-1A HSI Level 2 Data Product
NIU Li-ming 1, MENG Ji-hua 2,3, WU Bing-fang 2, CHEN Xue-yang 2, DU Xin 2, ZHANG Fei-fei 2
(1.Beijing Wisewatch IT Company, Beijing 100101, China; 2.Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China; 3.State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101,China)
Download: PDF(2068 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  To deal with the Level 2 HSI data from the newly-launched HJ-1A satellite, this paper introduced in details the entire flow and relevant algorithms for data preprocessing. The introduction includes calibration, vertical stripes elimination, and atmospheric correction geometric correction. Standard spectral reflectance products with precise geo-locations were produced. Spectral reflectance data from EO-1 Hyperion of close dates was used to simulate the band reflectance of HJ-1A HSI. Comparisons of spectral reflectance data between simulated and actual HJ-1A HSI were made to validate the effect of the data preprocessing. The average correlation coefficient of spectral reflectance between actual and simulated HJ-1A HSI is 0.947 with its standard deviation being 0.017, suggesting a high consistency. The mean and standard deviation of differential bands between real and simulated HJ-1A HSI are close to 0. The result shows that the reflectance from HJ-1A HSI is consistent with that of simulated data from Hyperion, and hence the data processing flow could provide necessary support for quantitative use of HJ-1A HSI data.
Keywords Remote sensed imagery      Automatic interpretation      Present condition      Development trend     
: 

TP 75

 
Issue Date: 22 March 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
YANG Guang
LIU Xiang-nan
Cite this article:   
YANG Guang,LIU Xiang-nan. Research on Standard Preprocessing Flow for HJ-1A HSI Level 2 Data Product[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 77-82.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.01.15     OR     https://www.gtzyyg.com/EN/Y2011/V23/I1/77
[1]中国资源卫星应用中心.环境和灾害监测预报小卫星星座AB星用户手册[Z].北京:中国资源卫星应用中心,2009.
[2]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.
[3]张东,张鹰,李欢.海岸带星载高光谱遥感影像预处理方法[J].海洋科学进展,2009,27(1):92-97.
[4]李传荣,贾媛媛,胡坚,等.HJ-1光学卫星遥感应用前景分析[J].国土资源遥感,2008(3):1-3.
[5]李石华,角媛梅.环境与灾害监测预报小卫星A星CCD影像质量评价[J].红外技术,2009,31(9):167-172.
[6]Datt B,McVicar T R,Van Niel T G,et al.Pre-processing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indices[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1246-1259.
[7]Boardman J W,Kruse F A.Automated Spectral Analysis:A Geological Example Using AVIRIS Data[C]//Proceedings of 10th Thematic Conference on Geologic Remote Sensing.Ann Arbor North Grapevine Mountains,Nevada,Environmental Research Institute of Michigan,1994,407-418.
[8]Research System,FLAASH,Boulder[M/OL].CO:Research System,Inc,2003.http://www.rsinc.com/envi/flaash.asp
[9]David G,Goodenough,Andrew D,et al.Processing Hyperion and ALI for Forest Classification [J].IEEE Transaction on Geoscience and Remote Sensing,2003,41(6):1321-1331.
[10]Chen C M,Hepner G F,Forster R R.Fusion of Hyperspectral and Radar Data Using the IHS Transformation to Enhance Urban Surface Features[J].ISPRS Journal of Photogrammetry & Remote Sensing,2003,58:19-30.
[1] XIONG Sheng-Qing. THE PROGRESS AND DEVELOPMENT TREND OF THE APPLICATION OF REMOTE SENSING TO LAND AND RESOURCES[J]. REMOTE SENSING FOR LAND & RESOURCES, 2007, 19(4): 1-6.
[2] YANG Guang, LIU Xiang-nan . THE PRESENT RESEARCH CONDITION AND DEVELOPMENT TREND OF REMOTELY SENSED IMAGERY INTERPRETATION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(2): 7-10,15.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech