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عدد المساهمات : 18959 التقييم : 35383 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: كتاب Computational Geophysics Adjustment Models in 3D Geomatics and Computational Geophysics السبت 04 مايو 2024, 3:10 am | |
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أخواني في الله أحضرت لكم كتاب Computational Geophysics Adjustment Models in 3D Geomatics and Computational Geophysics With Matlab Examples Volume 4 Bashar Alsadik Faculty member at Baghdad University – College of Engineering – Iraq (1999–2014) Research assistant at Twente University – ITC faculty – The Netherlands (2010–2014) Member of the International Society for Photogrammetry and Remote Sensing ISPRS
و المحتوى كما يلي :
Table of contents 1. Statistical Introduction 2. Propagation of Errors 3. Least Squares Adjustment Procedures 4. Common Observation Models and Their Adjustment in Geomatics 5. Adjustment Using General Observation Model 6. Adjustment with Constraints 7. Unified Approach of Least Squares 8. Fitting Geometric Primitives with Least Squares 9. 3D Transformation and Co-Registration 10. Kalman Filter 11. Introduction to Adjustment with Levenberg-Marquardt Method 12. Post Analysis in Adjustment Computations Appendix: MATLAB Code for Generic 2D Geodetic Networks Adjustment References Index Note: Page numbers followed by f indicate figures, t indicate tables and b indicate boxes. A Accidental errors, 3, 3f Accuracy, 4, 4f Additional constraints, 188 Adjusted points, ellipsoid of errors, 223–224f, 229f, 238f Adjustment, 53, 55f, 215. See also Least squares adjustment with condition equations method, 62, 64 with inner constraints, 198 by observation equations, 61, 63 Airplane flight simulation, 313–326, 319f, 321f Angle observation model, 95–100 Angular 2D resection, 99, 100f Arithmetic mean, 6 Azimuth angle of ellipsoid of errors, 78 of laser beam, 29–30 2D observation model, 90, 110 Azimuth direction intersection, 164, 164f Azimuth observation model, 91 B Body waves, 143 C Cartesian coordinates, 100 CDF. See Cumulative distribution function (CDF) Circle parameters, 188 Collinearity equations, 124–125 Computational geophysics, 141 Condition equations, 55, 55f, 59–66 Constrained adjustment. See also Free net adjustment additional parameters, 188–198, 194f correction vector, 171 direct method, 171, 174 geometric/physical conditions, 169, 169f Helmert method, 171–173 image triangulations, 177–181, 178f inner constraints, 198–214 LaGrange multipliers, 171 normal equations, 171 over-weighting method, 172–185 perpendicularity, 182–184b, 185 unified, 233 Control points, 155–167, 157f Coregistration concept, 273f of point clouds, 274f, 279f, 280, 285f target-based, 274f Cosine rule, 119 Cumulative distribution function (CDF), 11–13 D Damped least-squares method, 327 Damping factor, 328–329 Data snooping, 351–363, 352–353b, 357–361b Datum defect, 199–200 Degree of freedom, 6, 53 Detection, identification, and adaptation (DIA) test, 349–382 Direct adjustment method, 171, 174 Direct linear transformation (DLT), 83–84 Dynamic model for 3D state case, 302 of filtering, 320–326, 321f E Earthquake hypocenter and epicenter, 144f and least squares adjustment, 144–151 P and S waves, 143, 143f EKF. See Extended Kalman Filter (EKF) Ellipse of errors, 67–70, 70f free net adjustment, 202, 203f Ellipsoid of errors, 67, 76–83, 78f adjusted points, 223–224f, 229f, 238f constrained adjustment, 177, 177f free adjustment, 208f, 212, 214f image triangulation, 135, 135f Lidar sensor, 31–34, 32f vertical angle observation, 113f Engineering construction project, 1, 2f Error propagation. See Propagation of errors 411Errors classification, 348 definition, 2 preanalysis of, 43–51 Extended Kalman Filter (EKF), 299 External reliability, 385 F Fitting circle in 3D space, 256–258 cylinder, 261f, 263–271, 264f, 266–268b, 268f plane, 248–250 sphere, 251 2D circle, 258–263 3D line, 247f, 250–256 Forward problem, 141, 142f Free net adjustment, 199 convergence, 213f of 2D network, 199, 206, 206f for 3D networks, 200 ellipse of errors, 202, 203f ellipsoid of errors, 208f, 212, 214f MATLAB code, 209–210 observation equations, 207 pseudoinverse matrix, 211 variance covariance matrix, 200 variance of unit weight, 200 Fundamental matrix, 83–84 G Gaussian curve, 304 Gauss Newton (GN) method, 327–329 Geiger’s method, 144 Generalized inverse, 47–51 General least squares model concept of, 153, 154f covariance matrix, 155 normal equations, 155 residuals vector, 155 sphere fitting, 155–159b 3D forward intersection by angles, 163–167b Geodetic network, 67, 67f GN method. See Gauss Newton (GN) method Goodness of fit test, 346–348, 347f GPR. See Ground penetration radar (GPR) Gradient descent, 328, 328f Gross errors, 2 Ground penetration radar (GPR), 216 H Helmert method, 171–173, 179, 199, 202, 207 Homogeneous least squares adjustment, 83, 245–247b image rectification by homography, 84–88 MATLAB code, 88 singular value decomposition, 83, 87 Hypothesis testing, classification of error in, 348–349, 348t, 349f I ICP. See Iterative closest point (ICP) Image pose/resection, 337–340b Image rectification, 84 Image space resection, 125–135 Image triangulation/intersection with constraints, 177–181, 178f least squares adjustment, 136–141 observation equation, 134–135, 140 Image warping, 85–88 Inner constraints. See Free net adjustment Internal reliability, 385 Inverse problem, 142, 142f Iterative closest point (ICP), 275 J Jacobian matrix, 25–26 Lidar sensor error estimation, 30 quadrilateral polygon area computation, 40–43 triangular polygon area computation, 35 K Kalman filter applications, 299–300 concepts, 299 corrections and update, 303–304, 303f distributions, 305 efficiency of, 299 prediction, 300–303, 300–301f structural deformation monitoring, 306–312, 307f, 312f workflow, 305f Keystone distortion, 85–88 Kronecker product, 44–47, 50, 291 L LaGrange multipliers, 56, 59, 153, 170 Laser scanner, 216 Laser scanning, 29–34 Least squares, 53 Least squares adjustment, 15–17. See also Homogeneous least squares adjustment angle observation model, 95–100 Azimuth observation model, 91 condition equations model, 55f, 59–66 earthquake location and, 144–151 ellipse of errors, 68–70, 70f ellipsoid of errors, 76–83, 78f homogeneous system, 83–88 412 INDEXimage space resection, 125–135 image triangulation/intersection, 136–141 nonhomogeneous system, 85, 87 oblique angular resection, 118–124 observation equations model, 55f, 56–59 properties, 55–56 relative ellipse of errors, 70–76, 83f seismic waves and earth’s interior, 143 2D distance observation, 90–93 3D distance observation model, 100–105 3D line intersection model, 113–118 unified approach, 215–216, 215f, 233–242 vertical angle observation model, 105–113 Levenberg-Marquardt (LM) method, 327–329 Lidar sensors, 29 Linear least squares-based techniques, 245–247b Linear quadratic estimation (LQE), 299 LM method. See Levenberg-Marquardt (LM) method M MAD. See Median absolute deviation (MAD) MATLAB code condition and observation adjustment method, 66 constraints with additional parameters, 194–196, 198 cumulative distribution function, 11–12 earthquake location problem, 148–149, 151 ellipsoid of errors, 79–80 free net adjustment, 209–210 for general least squares, 161–162 homography matrix, 88 image resection problem, 130–136 image triangulation, 137–138 Kalman filtering, 321–325 Kronecker product, 46 Lidar sensor error estimation, 33 normal distribution curve, 10 oblique angle resection, 121, 123 perpendicularity constrained adjustment, 185–187 polygon area computation, 38 pseudoinverse/generalized inverse computation, 50 relative ellipse of errors, 73–75 robust estimation, 368 sphere fitting, 254 3D distance observation, 103–105 weighted mean, 19, 21 Matrix form, 25–28 Median absolute deviation (MAD), 13, 364–365 Minimal constraint, 199 Misfit, 329–334 Mixed adjustment model, 153 Mixed 2D observations, 97, 97f Mixed triangulation-trilateration network, 67, 67f Mobile mapping system (MMS), 216 Model norm, 329–334 Most probable value (MPV), 2, 5, 7 Multidata collection system, 216f N Newton optimization method, 328, 328f NLLS. See Nonlinear least squares problems (NLLS) Nonhomogeneous least squares adjustment, 85, 87 Nonlinear least squares, 327 of 3D similarity transformation, 275–277, 276f Nonlinear least squares problems (NLLS), 327 Nonlinear observation equations, 89–90 Normal distribution curve, 7–11 Normal equation system, 56 O Oblique angle 3D resection model, 118–124 Observation equation model adjustments by, 54–59, 55f, 63 angle, 96–100, 96f azimuth, 94 free net adjustment, 207 image space resection, 127 image triangulation/intersection, 134–135, 140 line intersection, 3D space, 113–118 2D distance, 90, 92–93 3D distances, 100–105 for vertical angles, 106–107 Overlapped images, of facade, 133, 140f Over-weighting method, 172–185 P Panoramic camera, 216 Perpendicularity constrained adjustment, 182–184b Perspective distortion, 85–88, 86f Plane fitting, 245f Point coordinates, 188 Polygon area computation, 34–42 Positional constraints, 199–200 Postadjustment analysis, 349 Postanalysis techniques, 345, 345f Preanalysis, 23 of image intersection, 48f Kronecker product technique, 44–47 using pseudoinverse/generalized inverse, 47–51 Precision, 4, 4f Probable error, 13–15 Propagation of errors, 23f definition, 23 facade area measurement, 34f in laser scanning, 29–34 law, 24–25 INDEX 413Propagation of errors (Continued) matrices, 25–28 of parallelogram tank, 24–25, 25f preanalysis by pseudoinverse/generalized inverse, 47–51 preanalysis using Kronecker product, 44–47 quadrilateral polygon area computation, 39–43 rectangular facade area, 42f triangular polygon area computation, 34–35, 37f Pseudo code, 334–344 Pseudoinverse, 47–51, 211 Q Quadrilateral polygon area computation, 39–43 R Racing athletes, 1, 2f Random errors, 3, 3f Random Sample Consensus (RANSAC) algorithm, 376–382, 377–378b, 378f Rank defect, 199–200 RANSAC algorithm. See Random Sample Consensus (RANSAC) algorithm Rectangular facade area, 42f Redundancy, 6, 54 Redundancy number, 384–385 Relative ellipse of errors, 70–76, 81, 83f Reliability, 5, 5f Reliability computations, 382–385 Resection, image space, 125–135 Residual error, 5 Robust estimation technique, 363–376, 365–367b Rodrigues rotation formula, 250f, 252f, 256–257f, 259–261 Root Mean Squared Error (RMSE), 6–7 Rotational constraints, 199–200 S Satellite navigation system (GNSS), 216 Scale constraint, 199–200 Seismic waves, 143 Seismometer, 143 Singular value decomposition (SVD), 47, 83, 85 Slope angle error estimation, 28f Sphere equation, 155–156 Spherical trigonometry law, 118 Standard deviation, 6 Standard ellipse of errors, 69–70, 72 Standard error, 6 Surface waves, 143 SVD. See Singular value decomposition (SVD) Systematic errors, 2, 3f T Target-based coregistration, 274f Taylor series expansion, 53 Terrestrial laser scanning (TLS), 29f, 274 2D circle, fitting, 258–263 2D models angle observation, 95–100 Azimuth observation, 94–95 distance observation, 90–93 3D circle fit, 252f least squares fitting of, 258f 3D intersection, by distances, 101f, 330–333b 3D similarity transformation close form solution, 277–291, 279–285b to coregister the point clouds, 279f, 280, 285f nonlinear least squares solution, 275–277, 276f 3D space fitting circle in, 256–258 polygon area computing, 100 3D transformations, 273f, 274 computations, 273 planes to planes transformation, 296–298, 297–298b points to points transformation, 275–291 point to plane transformation, 291–296, 293–296b propagation of errors in, 289–291b 3D line best fit, 247–248f intersection model, 113–118 3D models distance observation, 100–105 line intersection, 113–118 oblique angular resection, 118–124 Travel time, seismic waves, 143 Triangular polygon area computation, 34–35, 37f Tri-angulation network, 67, 67f Trilateration 2D geodetic network, 93, 93f, 98f Trilateration network, 67, 67f Tylor’s theorem, 24–25 U Uncertainty, 3 Unified adjustment, 217–232, 218–222b, 224–229b Unified approach of least squares adjustment, 215–216, 215f of least squares with constraints, 233–242, 235–236b V Variance, 6 Variance-covariance matrix, 24, 26, 28 ellipse of errors, 68 for ellipsoid of errors, 82–83 free net adjustment, 200 414 INDEXrelative ellipse of errors, 71 triangular polygon area computation, 35 Variance of unit weight, 57 Variation of coordinates method, 89 Vectors cross product, 35f Velodyne scanning, 30 Vertical angle observation model, 105–113 W Weighted mean, 17–22 Weight matrix, 56, 157, 164 Z Zenith angle, 105 #ماتلاب,#متلاب,#Matlab,#مات_لاب,#مت_لاب,
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