Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-27T04:38:33.304Z Has data issue: false hasContentIssue false

16 - Challenges, solutions, and applications of accurate multiangle image registration: Lessons learned from MISR

from PART IV - Applications and Operational Systems

Published online by Cambridge University Press:  03 May 2011

By
Veljko M. Jovanovic ,
David J. Diner  and
Roger Davies
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Veljko M. Jovanovic
Affiliation:
California Institute of Technology, California
David J. Diner
Affiliation:
California Institute of Technology, California
Roger Davies
Affiliation:
The University of Auckland, New Zealand
Jacqueline Le Moigne
Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu
Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman
Affiliation:
Loyola University Maryland
Get access

Summary

Abstract

A novel approach implemented to meet coregistration/georectification requirements and continuous data-intensive processing demands of the Multi-angle Imaging SpectroRadiometer (MISR) science data system has been in operation since the beginning of on-orbit data acquisition in February 2000. Remote sensing image data are typically only radiometrically and spectrally corrected as a part of standard processing, prior to being distributed to investigators. In the case of MISR, with its unique configuration of nine fixed pushbroom cameras, continuous and autonomous coregistration and geolocation of the data are essential prior to application of any subsequent scientific retrieval algorithm. A fully automated system for continuous orthorectification, including removal of errors related to camera internal geometry, spacecraft attitude data, and surface topography, has been implemented.

The challenges involved in employing such a system range from purely algorithmic issues to those related to limitations on computational resources and data volumes. Processing algorithms had to be designed so that ~35 GB of image data per day are orthorectified without interruption and with high fidelity, as verified by an automated quality assessment process. We adopted a processing strategy that distributes the effort between the MISR Science Computing Facility at the Jet Propulsion Laboratory in Pasadena, CA and the Distributed Active Archive Center (DAAC) at the NASA Langley Research Center, Hampton, VA.

Accurate geolocation and coregistration of multiangle, multispectral MISR data is critical for the higher-level science retrieval algorithms.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 355 - 382
Publisher: Cambridge University Press
Print publication year: 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ackermann, F. (1984). Digital image correlation: Performance and potential application in photogrammetry. The Photogrammetry Record, 11(64), pp. 429–439.CrossRefGoogle Scholar
Bailey, G. B., Carneggie, D., Kieffer, H., Storey, J. C., Jovanovic, V. M., and Wolfe, R. E. (1997). Ground Control Points for Calibration and Correction of EOS ASTER, MODIS, MISR and Landsat 7 ETM+ Data. SWAMP GCP Working Group Final Report, USGS, EROS Data Center, Sioux Falls, SD.Google Scholar
Bintanja, R. and Reijmer, C. H. (2001). Meteorological conditions over Antarctic blue-ice areas and their influence on the local surface mass balance. Journal of Glaciology, 47(156), 37–50.CrossRefGoogle Scholar
Bothwell, G., Hansen, E. G., Vargo, R. E., and Miller, K. C. (2002). The MISR science data system: Its products, tools, and performance. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1467–1476.CrossRefGoogle Scholar
Braswell, B. H., Hagen, S. C., Salas, W. A., and Frolking, S. E. (2003). A multivariable approach for mapping sub-pixel land cover distributions using MISR and MODIS: Application in the Brazilian Amazon. Remote Sensing of Environment, 87, 243–256.CrossRefGoogle Scholar
Castleman, K. R. (1979). Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Chopping, M. J., Su, L. H., Laliberte, A., Rango, A., Peters, D. P. C., and Martonchik, J. V. (2006). Mapping woody plant cover in desert grasslands using canopy reflectance modeling and MISR data. Geophysical Research Letters, 33, L17402, doi: 10.129/2006 GL027148.CrossRefGoogle Scholar
Davies, R., Horváth, A., Moroney, C., Zhang, B., and Zhu, Y. (2007). Cloud motion vectors from MISR using sub-pixel enhancements. Remote Sensing of Environment, MISR Special Issue, 107, 194–199.CrossRefGoogle Scholar
Dennis, J. E. and Schnabel, R. B. (1983). Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchick, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A. W., Gordon, H. R., Muller, J. P., Myneni, R., Sellers, P. J., Pinty, B., and Verstraete, M. M. (1998). Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1072–1087.CrossRefGoogle Scholar
Diner, D. J., Braswell, B. H., Davies, R., Gobron, N., Hu, J., Jin, Y., Kahn, R. A., Knyazikhin, Y., Loeb, N., Muller, J.-P., Nolin, A. W., Pinty, B., Schaaf, C. B., Seiz, G., and Stroeve, J. (2005). The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces. Remote Sensing of Environment, 97(4), 495–518.CrossRefGoogle Scholar
Diner, D. J., Clothiaux, E., and Di Girolamo, L. (1999a). Level 1 Cloud Detection Algorithm Theoretical Basis Document. JPL Tech. Doc. D-11397, Rev. B. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA.
Diner, D. J., Davies, R., DiGirolamo, L., Moroney, C., Muller, J.-P., Paradise, S., Wenkert, S., and Zong, J. (1999b). Level 2 Cloud Detection and Classification Algorithm Theoretical Basis Document. JPL Tech. Doc. D-11399, Rev. D. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA.
Förstner, W. (1982). On the geometric precision of digital correlation. In Proceedings of the Commission III International Society for Photogrammetry and Remote Sensing, Helsinki, Finland, Vol. XXIV, pp. 176–189.Google Scholar
Förstner, W. and Gülch, E. (1987). A fast operator for detection and precise location of distinct points, corners and centers of circular features. In Proceedings of the International Society for Photogrammetry and Remote Sensing Intercommission Workshop on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, pp. 281–305.Google Scholar
Haralick, R. M. and Shapiro, L. G. (1993). Computer and Robot Vision. Reading, MA: Addison-Welsey.Google Scholar
Horváth, Á. and Davies, R. (2001). Feasibility and error analysis of cloud motion wind extraction from near-simultaneous multiangle MISR measurements. Journal of Atmospheric Oceanic Technology, 18, 591–608.2.0.CO;2>CrossRefGoogle Scholar
Hu, J., Tan, B., Shabanov, N., Crean, K. A., Martonchik, J. V., Diner, D. J., Knyazikhin, Y., and Myneni, R. B. (2003). Performance of the MISR LAI and FPAR algorithm: A case study in Africa. Remote Sensing of Environment, 88, 324–340.CrossRefGoogle Scholar
Jovanovic, V. M., Bull, M., Smyth, M. M., and Zong, J. (2002). MISR in-flight camera geometric model calibration and achieved georectification performances. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1512–1519.CrossRefGoogle Scholar
Jovanovic, V. M., Moroney, C., and Nelson, D. (2007). Multi-angle geometric processing for globally geo-located and co-registered MISR image data. Remote Sensing of Environment, 107(1–2), 22–32.CrossRefGoogle Scholar
Jovanovic, V. M., Smyth, M. M., Zong, J., Ando, R., and Bothwell, G. W. (1998). MISR photogrammetric data reduction for geophysical retrievals. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1290–1301.CrossRefGoogle Scholar
Kahn, R. A., Gaitley, B. J., Crean, K. A., Diner, D. J., Martonchik, J. V., and Holben, B. N. (2005). MISR global aerosol optical depth validation based on two years of coincident AERONET observations. Journal of Geophysical Research, 110, D10S04, doi: 10.129/2004JD004706.CrossRefGoogle Scholar
Korechoff, R. P., Jovanovic, V. M., Hochberg, E. B., Kirby, D. M., and Sepulveda, C. A. (1996). Distortion calibration of the MISR linear detector arrays. In Proceedings of the SPIE Conference on Earth Observing Systems, Denver, CO, Vol. 2820, pp. 174–183.CrossRefGoogle Scholar
Logan, T. L. (1999). EOS/AM-1 Digital Elevation Model (DEM) Data Sets: DEM and DEM Auxiliary Datasets in Support of the EOS/Terra Platform. JPL Tech. Doc. D-013508. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA.Google Scholar
Martonchik, J. V., Diner, D. J., Kahn, R., Gaitley, B., and Holben, B. N. (2004). Comparison of MISR and AERONET aerosol optical depths over desert sites. Geophysical Research Letters, 31, L16102, doi:10.1029/2004GL019807.CrossRefGoogle Scholar
Mikhail, E. M. (1976). Observations and Least Squares. New York: Harper & Row.Google Scholar
Moroney, C., Davies, R., and Muller, J. P. (2002a). Operational retrieval of cloud-top heights using MISR. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1532–1540.CrossRefGoogle Scholar
Moroney, C., Horváth, A., and Davies, R. (2002b). Use of stereo-matching to coregister multiangle data from MISR. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1541–1546.CrossRefGoogle Scholar
Muller, J. P., Mandanayake, A., Moroney, C., Davies, R., Diner, D. J., and Paradise, S. (2002). MISR stereoscopic image matchers: Techniques and results. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1547–1559.CrossRefGoogle Scholar
Nolin, A. W., Fetterer, F. M., and Scambos, T. A. (2002). Surface roughness characterizations of sea ice and ice sheets: Case studies with MISR data. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1605–1615.CrossRefGoogle Scholar
Paderes, F. C., Mikhail, E. M., and Fagerman, J. A. (1989). Batch and on-line evaluation of stereo SPOT imagery. In Proceedings of the Annual American Society of Photogrammetry and Remote Sensing and the American Congress on Surveying and Mapping Convention, Baltimore, MD, Vol. 3, pp. 31–40.Google Scholar
Pinty, B., Widlowski, J.-L., Gobron, N., Verstraete, M. M., and Diner, D. J. (2002). Uniqueness of multiangular measurements–part I: An indicator of subpixel surface heterogeneity from MISR. IEEE Transactions on Geoscience and Remote Sensing, 40(7), 1560–1573.CrossRefGoogle Scholar
Seiz, G., Davies, R., and Grün, A. (2006). Stereo cloud-top height retrieval with ASTER and MISR. International Journal of Remote Sensing, 27(9), 1839–1853.CrossRefGoogle Scholar
Snyder, J. P. (1987). Map Projections – A Working Manual. United States Geological Survey Professional Paper 1395, U.S. Government Printing Office, Washington, DC.Google Scholar
Widlowski, J.-L., Pinty, B., Gobron, N., Verstraete, M. M., Diner, D. J., and Davis, A. B. (2004). Canopy structure parameters derived from multi-angular remote sensing data for terrestrial carbon studies. Climatic Change, 67(2–3), 403–415.CrossRefGoogle Scholar
Yang, Y., Di Girolamo, L., and Mazzoni, D. (2007). Selection of the automated thresholding algorithm for the Multi-angle Imaging SpectroRadiometer camera-by-camera cloud mask over land. Remote Sensing of Environment, MISR Special Issue, 107(1–2), 159–171.CrossRefGoogle Scholar
Zong, J. (2002). Multi-image tie-point detection applied to multi-angle imagery from MISR. In Proceedings of the Commission III International Society of Photogrammetry and Remote Sensing Symposium on Photogrammetric Computer Vision, Graz, Austria, p. A-424.Google Scholar
Zong, J., Davies, R., Muller, J. P., and Diner, D. J. (2002). Photogrammetric retrieval of cloud advection and top height from the Multi-angle Imaging Spectro-Radiometer (MISR). Photogrammetric Engineering & Remote Sensing, 68(8), 821–829.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×