Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: XX, 530 Seiten

Type of content:: Konferenzschrift

DOI:: 10.14463/GBV:1067490280

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Reihe:: International archives of photogrammetry and remote sensing (30,2)

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Allam, Mosaad; Plunkett, Gordon

Editor:: International Society for Photogrammetry and Remote Sensing; Kanada, Surveys, Mapping and Remote Sensing Sector

Author:: Symposium Systems for Data Processing, Analysis and Representation, 1994; Ottawa

Contributor:: International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session F-1 WG II/1 - Real-Time Mapping Technologies - Automatic Orientation of Sensors]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: GPS CONTROLLED TRIANGULATION OF SINGLE FLIGHT LINES Ayman Habib, Kurt Novak

Document type:: Monograph

Structure type:: Chapter

nd, in order control is ent for the lat must not ulation this ervations at are well er hand this on since the are almost id the flight 1 points are | (Alobaida, e of strip loys GPS er with the nd. In this g the linear t since the lighway or ring vehicle impossible ct physical ]: ints are the these an feature. image onding a ment. verview of A detailed jangulation s for both ns 4 and 5, ations are ation rvations of in bundle sumed that sure times sition the additional Xs x dx = * xb uds SAU (1) Z dz G o with: XY Observed coordinates of GG G the GPS antenna, X.X452 unknown coordinates of o 0 o the perspective center, R unknown rotation matrix associated with the image, dx, dy, dz GPS antenna eccentricity from the perspective center with respect to the image coordinate system. This model takes into account the eccentricity of the. GPS antenna with respect to the perspective center of the aerial camera. This offset must be determined by pre-flight calibration using terrestrial techniques. We could treat these shift parameters as constants or as observations; the latter approach allows us to consider the accuracy of the calibration results. If they are treated as constants, we have regular Observation equations, if they are considered observations, we have to deal with condition equations with parameters. Additionally, linear "drift" parameters can be introduced as unknowns for each of the coordinates (Friess, 1992): Xs X dxV [ax\ [bx x = Y +R*( dy )+( ay ) +( by ]*dt (2) © A dz az bz with: ax, ay, az shift parameters, bx, by, bz time dependent parameters, dt is the time interval. These parameters are very effective for correcting the datum transformation and cycle slips. They may be block-, strip-, or sub-strip invariant. If the GPS observations are continuous for the whole flight mission, one set of "drift" parameters is sufficient. If the satellite signal is interrupted, a new set of drift parameters has to be introduced. These parameters cause correlations and may lead to singularities. Therefore, a minimum of four ground control points and cross strips at the beginnings and ends of the strips are needed to produce accurate results (Gruen et al., 1993; Ackermann et al., 1993). For our particular application all control is available in WGS84, both the GPS observations at the aircraft and those along the linear feature. Therefore, we do not have to worry about the datum transformation problem. 3. Strip Triangulation with a Known Linear Feature on the Ground For this triangulation procedure we assume that GPS observations are available in the aircraft at the time of exposure. These observations are handled in the bundle adjustment according to the mathematical model shown in formula (1) As mentioned earlier, GPS controlled strip triangulation cannot be carried out without ground control, because all exposure stations are along a single flight line. Instead of ground control points, however, we utilize GPS observations along a linear feature on the ground which is approximately parallel to the flight line. These ground coordinates can be collected from a mobile mapping platform, e.g. the GPSVan (The Center For Mapping, 1991), that travels along the linear feature. The major problem of implementing features instead of points as observations for strip triangulation lies in the association of corresponding features on the ground and in the image. Since the collinearity equations are based on a point to point correspondence between image and object space, feature observations cannot be directly introduced in bundle adjustment. An extended model has to be developed to include these observations. It is explained below. As a first step the coordinates of arbitrary points are measured along the linear feature in the image. These points are selected individually in each image. They need not to be common in successive images, because they are not used as tie points. An analytical function, usually a low order polynomial, is fit to the measured image coordinates. It represents the linear feature in that particular image. Each image has its own function which is of the form (3). The order of the polynomial can be chosen either by the operator or automatically. The polynomial coefficients and their variance-covariance matrix are determined by a least squares adjustment in a separate step for each image individually. f; y1,,0,,,4, 02 js 8p )=0 (3) with: 205

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