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Journal of Mountain Science

, Volume 10, Issue 5, pp 754–767 | Cite as

Auto-registration and orthorecification algorithm for the time series HJ-1A/B CCD images

  • Jin-hu Bian
  • Ai-nong Li
  • Hua-an Jin
  • Guang-bin Lei
  • Cheng-quan Huang
  • Meng-xue Li
Article

Abstract

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multispectral data of small constellation for environmental and disaster mitigation (HJ-1A/B) which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels (the average residuals were 37.8 m, and standard deviations were 19.8 m). The accuracies of 45.96% validation points (VPs) were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, percent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

Keywords

HJ time series images Autogeocorrection Topographic correction Wide coverage CCD cameras 

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References

  1. Ali MA, Clausi DA (2002) Automatic registration of SAR and visible band remote sensing images. IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Symposium on Remote Sensing 1331–1333. Toronto, Canada, June, 2002. DOI: 10.1109/IGARSS.2002.1026106Google Scholar
  2. Aguilar MA, Aguera F, Aguilar F J, et al. (2008) Geometric accuracy assessment of the orthorectification process from very high resolution satellite imagery for Common Agricultural Policy purposes. International journal of remote sensing 29(24): 7181–7197. DOI: 10.1080/01431160802238 393CrossRefGoogle Scholar
  3. Bamler R (1999) The SRTM mission: A world-wide 30 m resolution DEM from SAR interferometry in 11 days, Heidelberg, Germany: Wichmann.Google Scholar
  4. Bryant N, Zobrist A, Logan T (2003) Automatic co-registration of space-based sensors for precision change detection and analysis. IEEE International Geoscience and Remote Sensing Symposium, 1371–1373. Toulouse, France, July, 2003. DOI: 10.1109/IGARSS.2003.1294112Google Scholar
  5. Bunting P, Lucas R, Labrosse F (2008) An area based technique for image-to-image registration of multi-modal remote sensing data. IEEE International Geoscience and Remote Sensing Symposium, 212–215. Boston, USA, July, 2008. DOI: 10.1109/IGARSS.2008.4780065Google Scholar
  6. Cao D, Sun J, Yang B (2003) Fix and analysis of the wide coverage camera CCD. Spacecraft Recovery & Remote Sensing 24(4): 5–9. (In Chinese)Google Scholar
  7. Chen J, Huan J, Hu J (2011) Mapping rice planting areas in southern China using the China Environment Satellite data. Mathematical and Computer Modelling 54(3–4): 1037–1043. DOI: 10.1016/j.mcm.2010.11.033CrossRefGoogle Scholar
  8. Chen P, Wang J, Liao X, et al. (2010) Using Data of HJ21A /B Satellite for Hulunbeier Grassland Aboveground Biomass Estimation. Journal of natural resources 25(7): 1122–1131. (In Chinese)Google Scholar
  9. Cui X, Liu S, Wei X (2012) Impacts of forest changes on hydrology: a case study of large watersheds in the upper reaches of Minjiang River watershed in China. Hydrology and Earth System Sciences 16(11): 4279–4290. DOI: 10.5194/hess-16-4279-2012CrossRefGoogle Scholar
  10. Elassal AA (1987) General cartographic transformation package (GCTP), Version II. (http://www.ngs.noaa.gov/PUBS_LIB/GeneralCartographicTransformationPackage_v2_TR_NOS124_CGS9.pdf, accessed on 2013-07-06)Google Scholar
  11. Dai XL, Khorram S (1999) A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Transactions on Geoscience and Remote Sensing 37(5): 2351–2362. DOI: 10.1109/36.789634CrossRefGoogle Scholar
  12. Eugenio F, Marques F, Marcello J (2002) A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery. IEEE International Geoscience and Remote Sensing Symposium and 24th Canadian Symposium on Remote Sensing, 3390–3392. Toronto, Canada, June, 2002. DOI: 10.1109/IGARSS.2002.1027192CrossRefGoogle Scholar
  13. Fan Y, Ding M, Liu Z, et al. (2007) Novel remote sensing image registration method based on an improved SIFT descriptor. International Symposium on Multispectral Image Processing and Pattern Recognition, International Society for Optics and Photonics. WuHan, China, November, 2007. DOI: 10.1117/12.751479Google Scholar
  14. Gao F, Masek JG, Wolfe RE (2009) Automated registration and orthorectification package for Landsat and Landsat-like data processing. Journal of Applied Remote Sensing 3(1). DOI: 10.1117/1.3104620Google Scholar
  15. Grohman G, Kroenung G, Strebeck J (2006) Filling SRTM voids: The delta surface fill method. Photogrammetric Engineering and Remote Sensing 72(3): 213–216.Google Scholar
  16. Hengl T, Reuter H (2011) How accurate and usable is GDEM? A statistical assessment of GDEM using LiDAR data, Geomorphometry.Google Scholar
  17. Hirt C, Filmer M, Featherstone W (2010) Comparison and validation of the recent freely available ASTER-GDEM ver1, SRTM ver4.1 and GEODATA DEM-9S ver3 digital elevation models over Australia. Australian Journal of Earth Sciences 57(3): 337–347. DOI: 10.1080/08120091003677553CrossRefGoogle Scholar
  18. Huang CQ, Goward SN, Masek JG, et al. (2009) Development of time series stacks of Landsat images for reconstructing forest disturbance history. International Journal of Digital Earth 2(3): 195–218. DOI: 10.1080/17538940902801614CrossRefGoogle Scholar
  19. Storey J, Strande D, Hayes R, et al. (2006) LANDSAT 7 (L7) IMAGE ASSESSMENT SYSTEM (IAS) GEOMETRIC ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD). (http://landsat.usgs.gov/documents/LS-IAS-01_Geometric_ATBD.pdf, accessed on 2013-07-06)Google Scholar
  20. Jacobsen K (2010). Comparison of ASTER GDEMs with SRTM Height Models. EARSeL symposium 2010, Paris, 521–526Google Scholar
  21. Jia F, Wu Y, Huang Y, et al. (2009) Technology of the wide coverage CCD carmeras for HJ-1A/1B. Spacecraft engineering 6: 37–42. (In Chinese)Google Scholar
  22. Kennedy RE, Cohen WB (2003) Automated designation of tiepoints for image-to-image coregistration. International Journal of Remote Sensing 24(17): 3467–3490. DOI: 10.1080/ 0143116021000024249CrossRefGoogle Scholar
  23. Leprince S, Barbot S, Ayoub F, et al. (2007) Automatic and Precise Orthorectification,Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements. IEEE Transactions on Geoscience and Remote Sensing 45(6): 1529–1558. DOI: 10.1109/TGRS.2006.888937CrossRefGoogle Scholar
  24. Li AN, Jiang JG, Bian JH, et al. (2012) Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions. ISPRS Journal of Photogrammetry and Remote Sensing 67: 80–92. DOI: 10.1016/j.isprsjprs.2011.10.008CrossRefGoogle Scholar
  25. Li AN, Bian JH, Lei GB, et al. (2012) Estimating the Maximal Light Use Efficiency for Different Vegetation through CASA Model Combined with Time-series Remote Sensing Data and Ground Measurements. Remote sensing 4(12): 1–18. DOI: 10.3390/rs4123857Google Scholar
  26. Li C, Yin J, Zhao J, et al. (2011) Extraction of urban built-up land in remote sensing images based on multi-sensor data fusion algorithms. Intelligent Computing and Information Science: 243–248. DOI: 10.1007/978-3-642-18129-0_39CrossRefGoogle Scholar
  27. Li H, Manjunath BS, Mitra SK (1995) A Contour-Based Approach to Multisensor Image Registration. IEEE Transactions on Image Processing 4(3): 320–334. DOI: 10.1109/83.366480CrossRefGoogle Scholar
  28. Li X, Zheng L, Hu Z (2006) SIFT based automatic registration of remotely-sensed imagery. Journal of remote sensing 10(6): 885–892. (In Chinese)Google Scholar
  29. Luedeling E, Siebert S, Buerkert A (2007) Filling the voids in the SRTM elevation model — A TIN-based delta surface approach. ISPRS Journal of Photogrammetry and Remote Sensing 62(4): 283–294. DOI: 10.1016/j.isprsjprs.2007.05.004CrossRefGoogle Scholar
  30. Mao Z, Pan D, Huang H, et al. (2001) Automatic registration of SeaWiFS and AVHRR imagery. International Journal of Remote Sensing 22(9): 1725–1735. DOI: 10.1080/01431160118 420Google Scholar
  31. Michel R, Avouac JP (2002) Deformation due to the 17 August 1999 Izmit, Turkey, earthquake measured from SPOT images. Journal of Geophysical Research-Solid Earth 107(B4): 2062. DOI: 10.1029/2000JB000102CrossRefGoogle Scholar
  32. McAlpin D, Meyer F (2012) Multi-sensor data fusion for remote sensing of post-eruptive deformation and depositional features at Redoubt Volcano. Journal of Volcanology and Geothermal Research. DOI: 10.1016/j.jvolgeores.2012.08.006Google Scholar
  33. Puymbroeck NV, Michel R, Binet R, et al. (2000) Measuring earthquakes from optical satellite images. Applied Optics 39(20): 3486–3494. DOI: 10.1364/AO.39.003486CrossRefGoogle Scholar
  34. Schiek C (2004) Terrain change detection using ASTER optical satellite imagery along the Kunlun fault, Tibet. M.S. thesis, Univ. Texas, El Paso, TX, 2004. [Online]. (http://www.geo.utep.edu/pub/schiek/Cara_Schiek_Master_Thesis.pdf. (Accessed on 2013-07-06)Google Scholar
  35. Selkowitz DJ, Green G, Peterson B, et al. (2012) A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions. Remote Sensing of Environment. 121: 458–471. DOI: 10.1016/j.rse.2012.02.020CrossRefGoogle Scholar
  36. Steinwand D, Wivell C (1993) Landsat Thematic Mapper terrain coeerctions in LAS. HSTX Inter-Office Memo OAB8-21, USGS EROS Data Center, August 12, 1993.Google Scholar
  37. Townshend JRG, Justice CO, Gurney C, et al. (1992) The Impact of Misregistration on Change Detection. IEEE Transactions on Geoscience and Remote Sensing 30(5): 1054–1060. DOI: 10.1109/36.175340CrossRefGoogle Scholar
  38. Tucker CJ, Grant DM, Dykstra JD (2004) NASA’s global orthorectified landsat data set. Photogrammetric Engineering and Remote Sensing 70(3): 313–322.Google Scholar
  39. Yu B, Yan M, Wu F, et al. (2010) The rothorectification of Ultra-Width remote sensing image. Remote sensing for land & resources 3(85): 31–35. (In Chinese)Google Scholar
  40. Wong A, Clausi DA (2010) AISIR: Automated intersensor/ inter-band satellite image registration using robust complex wavelet feature representations. Pattern recognition letters 31(10): 1160–1167. DOI: 10.1016/j.patrec.2009.05.016CrossRefGoogle Scholar
  41. Zhao S, Qin Q, Zhang F, et al. (2011) Research on Using a Mono-Window Algorithm for Land Surface Temperature Retrieval from Chinese Satellite for Environment and Natural Disaster Monitoring(HJ-1B) Data. Spectroscopy and Spectral Analysis 31(6): 1552–1556. (In Chinese)Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jin-hu Bian
    • 1
    • 2
  • Ai-nong Li
    • 1
  • Hua-an Jin
    • 1
  • Guang-bin Lei
    • 1
    • 2
  • Cheng-quan Huang
    • 3
  • Meng-xue Li
    • 3
  1. 1.Digital Mountain and Remote Sensing Application Center, Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of GeographyUniversity of MarylandCollege ParkUSA

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