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Autonomous estimates of horizontal decorrelation lengths for digital elevation models

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dc.contributor.authorCorrada-Emmanuel, Andres
dc.contributor.authorSchultz, Howard
dc.contributor.departmentUniversity of Massachusetts - Amherst
dc.contributor.departmentUniversity of Massachusetts at Amherst
dc.date2023-09-22T20:35:32.000
dc.date.accessioned2024-04-26T09:32:57Z
dc.date.available2024-04-26T09:32:57Z
dc.date.issued2008-01-16
dc.description.abstractThe precision errors in a collection of digital elevation models (DEMs) can be estimated in the presence of large but sparse correlations even when no ground truth is known. We demonstrate this by considering the problem of how to estimate the horizontal decorrelation length of DEMs produced by an automatic photogrammetric process that relies on the epipolar constraint equations. The procedure is based on a set of autonomous elevation difference equations recently proposed by us. In this paper we show that these equations can only estimate the precision errors of DEMs. The accuracy errors are unknowable since there is no ground truth. Furthermore, consideration of the invariance properties of the equations make clear that their application is limited to an imaging sensor that is accurate in its determination of the vertical direction. The practicality of the algorithm for estimating the horizontal decorrelation length of precision errors is shown by application to a set of DEMs produced from images of a desert terrain.
dc.identifier.urihttps://hdl.handle.net/20.500.14394/9512
dc.relation.ispartofComputer Science Department Technical Report TR-08-02
dc.relation.urlhttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1000&context=cs_faculty_pubs&unstamped=1
dc.source.statuspublished
dc.subjectMachine Learning Photogrammetry Computer Vision
dc.subjectautonomous error estimation
dc.subjecthorizontal decorrelation length
dc.subjectdigital elevation models
dc.subjectComputer Sciences
dc.subject.categoryARRAY(0x55934e7ea4f8)
dc.titleAutonomous estimates of horizontal decorrelation lengths for digital elevation models
dc.typeunpublished_paper
dc.typearticle
digcom.contributor.authorisAuthorOfPublication|email:corrada@cs.umass.edu|institution:University of Massachusetts - Amherst|Corrada-Emmanuel, Andres
digcom.contributor.authorisAuthorOfPublication|email:hschultz@cs.umass.edu|institution:University of Massachusetts at Amherst|Schultz, Howard
digcom.identifiercs_faculty_pubs/1
digcom.identifier.contextkey1102766
digcom.identifier.submissionpathcs_faculty_pubs/1
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relation.isAuthorOfPublication0e0ac9fc-5c64-4310-9e0f-14beaacccd16
relation.isAuthorOfPublication.latestForDiscovery0e0ac9fc-5c64-4310-9e0f-14beaacccd16
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