1 / 28

Geometric Assessment of Remote Sensed Data

Geometric Assessment of Remote Sensed Data. Oct. 25 2005 Presented By: Michael Choate, SAIC U.S. Geological Survey, National Center for EROS Sioux Falls, SD. Outline and Introduction. Landsat 7 Image Assessment System (IAS) Background Expanding the use of IAS Ground Control Mensuration

sidone
Télécharger la présentation

Geometric Assessment of Remote Sensed Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Geometric Assessment of Remote Sensed Data Oct. 25 2005 Presented By: Michael Choate, SAIC U.S. Geological Survey, National Center for EROS Sioux Falls, SD

  2. Outline and Introduction • Landsat 7 Image Assessment System (IAS) • Background • Expanding the use of IAS • Ground Control • Mensuration • RESOURCESAT-1 Assessment • Statistics • Vector Plots • Conclusions

  3. Landsat Image Assessment System (1 of 2) • Responsible for assessment of image quality of Enhanced Thematic Mapper (ETM+) • Ensure compliance with radiometric and geometric requirements • Perform radiometric and geometric calibration of satellite and ETM+ • Calibration results and updates distributed through Calibration Parameter File (CPF) • IAS contains Image to Image (I2I) registration assessment tool • Provides numerical evaluation of accuracy of common bands of temporally distinct ETM+ images • No real restriction on image data sets that can be used, other sensor can be used in assessment • IAS contains Band to Band (B2B) registration assessment tool • Provides numerical evaluation of accuracy of between band registration within an image • No real restriction on image data sets that can be used, other sensor can be used in assessment

  4. Landsat Image Assessment System (2 of 2) • Expanding the IAS beyond ETM+ • LPGS-Lite used as prototype for Advanced Land Imager (ALI) assessment system (ALIAS) • IAS I2I and B2B used for assessment of other sensors and datasets • SurreySat • Orbview-3 • Digitized aerial photography

  5. Ground Control (1 of 2) • Landsat IAS built ground reference data sets called Geometric Supersites or just Supersites • Built from Digital Orthophoto Quadrangulars (DOQs) • DOQs are designed to meet national mapping accuracy standards of 1:24k maps, or ~6 meters • Inspection with highly accurate GPS surveyed locations showed most DOQs exceeded 6 meters accuracy • 1 meter DOQs reduced in resolution to match PAN band (15m for ETM+ and 10m for ALI) • DOQs are mosaiced to create a data set equal to one World Wide Reference 2 (WRS2) nominal swath/length • Image chips are pulled from DOQ mosaics • USGS 1 arc second DEMs used for ground control height • Currently 30 data sets available

  6. Ground Control (2 of 2) DOQ Mosaic Note that individual DOQ files are visible in the mosaic

  7. Ground Control (3 of 3) Landsat WRS-2 Supersite Locations (CONUS)

  8. Mensuration • Mensuration done with Grey Scale Correlation •Correlation points chosen as evenly displaced points throughput image files •Offset is calculated by fitting surface around peak location • Outliers removed by observing correlation characteristics and residual statistics

  9. Correlation Grey Scale Correlation X and Y Offset Calculate Peak

  10. RESOURCESAT-1 • Payload contains three imaging sensors • Linear Imaging Self Scanner IV (LISS-IV) • Ground sample distance of 5.8 meters • 3 bands • 70km swath (monochromatic) 23km (multispectral) • Linear Imaging Self Scanner III (LISS-III) • Ground sample distance of 23.5 meters • 4 bands • 141km swath • Advanced Wide Field Sensor (AWiFS) • Two separate sensor modules (AWiFS-A and AWiFS-B) • 4 bands • 370km swath for each camera (740km total)

  11. RESOURCESAT-1 Assessment (1 of 2) • Attempt to assess both the AWiFS and LISS-III sensors aboard the RESOURCESAT-1 platform • Given two areas of coverage • Arizona • Corresponds to Landsat WRS-2 path 37 row 37 • Acquisition date 6/29/2005 • Railroad Valley • Corresponds to Landsat WRS-2 path 40 row 33 • Acquisition date 8/10/2005 • Both images were orthorectified geocoded products • AWiFS Assessment • Image extent of AWiFS data set allowed only a very small portion of the image file to be compared to corresponding supersite • Issue made worse by comparing individual AWiFS data sets (A,B,C,D) independently • Independent study done to avoid double resampling AWiFS data sets (each data set map projected with different set of parameters)

  12. RESOURCESAT-1 Assessment (2-2) • Control covered only partial amount of multiple data sets • Band assessment made for all data sets • AWiFS A, B, C and D data sets assessed independently • LISS-III Assessment • DOQ control completely covered full image extent • Output included • residuals file containing point by point residual offset in line and sample direction • statistical file containing maximum, minimum, mean, standard deviation, and root mean squared error of residuals for line and sample directions • residuals vector plot

  13. Arizona Data Sets (AWiFS, LISS-III, DOQ) DOQ and LISS-III A C B D DOQ AWiFS-A,B,C,D 172000 m 172000 m 85800 m LISS-III DOQ and LISS-III 172000 m 172000 m 897000 m

  14. Full Resolution LISS-III to DOQ LISS-III DOQ

  15. AWiFS Image-to-Image

  16. AWiFS Band-to-band registration Arizona Rband = Reference Sband = Search StDevL = Standard deviation line StDevS = Standard deviation sample RMSEL = Root mean squared error line RMSES = Root mean squared error sample

  17. AWiFS Band-to-band registration Railroad Valley Rband = Reference Sband = Search StDevL = Standard deviation line StDevS = Standard deviation sample RMSEL = Root mean squared error line RMSES = Root mean squared error sample

  18. LISS-III Image-to-Image

  19. LISS-III Band-to-band registration Rband = Reference Sband = Search StDevL = Standard deviation line StDevS = Standard deviation sample RMSEL = Root mean squared error line RMSES = Root mean squared error sample Arizona Railroad Valley

  20. LISS-III Band registration vector plot (Arizona) Vectors scaled to show trend

  21. LISS-III Band registration vector plot (Railroad Valley) Vectors scaled to show trend

  22. LISS-III Image to Image Residuals (Arizona) Vectors scaled by 350 Vector residuals comparing LISS-III to DOQs LISS-III Arizona Data set

  23. LISS-III Image to Image Residuals (Railroad Valley) Vectors scaled by 350 Vector Residual Comparing LISS-III and DOQs LISS-III Railroad Valley Data Set

  24. Conclusions • Landat 7 Image Assessment System can be expanded for use beyond that of the Enhanced Thematic Mapper • Image extent of AWiFS data set difficult to assess with given ground control available • Approach using other type of control covering more area would work better • Mosaicing several Landsat scenes • National Land Cover Database (NLCD) • AWiFS data set band registration difficult to assess, more data sets would be helpful • LISS-III data sets showed good relative geometric accuracy to that of DOQs • LISS-III vector plots for band registration residuals show possibility of small misalignment

  25. Back Up Slides

  26. LISS-III Band registration vector plot (Arizona)

  27. AWiFS Railroad Valley Data Set Showing DOQ Coverage DOQ Coverage Red out line is equal to approximately one Landsat WRS image extent

  28. LISS-III Railroad Valley Data Set Showing DOQ Coverage DOQ Mosaic LISS-III

More Related