XVIIIth Congress: XVIIIth Congress

kraus, karl; waldhäusl, peter

Bibliographic data

Multivolume work

Persistent identifier:: 1667435949

Title:: XVIIIth Congress

Sub title:: Vienna, Austria 1996

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667435949

Language:: English

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1667437070

Title:: XVIIIth Congress

Scope:: 230 Seiten

Year of publication:: 1996

Place of publication:: Vienna

Publisher of the original:: Austrian Society of Surveying and Geoinformation

Identifier (digital):: 1667437070

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(31,B1)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist anhand des Copyrightjahrs ermittelt.

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

Editor:: Kraus, Karl; Waldhäusl, Peter

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 18., 1996, Wien; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

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:: Volume

Collection:: Earth sciences

Chapter

Title:: ROBUST PROCEDURES FOR DATA PREPROCESSING, TESTING AND ARCHIVING Tamara Bellone, Bruno Crippa, Luigi Mussio

Document type:: Multivolume work

Structure type:: Chapter

ROBUST PROCEDURES FOR DATA PREPROCESSING, TESTING AND ARCHIVING Tamara Bellone* Bruno Crippa** wire «Xk Luigi Mussio * DIGET - Politecnico di Torino (Italy) Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy ** DIIAR - Politecnico di Milano (Italy) Piazza Leonardo da Vinci, 32 - 20133 Milano, Italy bruno@ipmtf4.topo.polimi.it Commission I, Working Group 6 Keywords: Accuracy, reliability, robustness. Abstract Robust estimation techniques are essentially downweighting methods and, among them, redescending estimators are the most promising ones, because their breakdown point is often very high. A method, recently proposed by Rousseeuw and Leroy, is here presented and applications to outlier identification in photogrammetry are discussed. 1. The problem Outlier identification and solution methods insensitive to outliers are a main topic in the photogrammetric and gen- erally survey and mapping community and many significant results have been established. The fundamental concepts of internal and external reliability introduced by Baarda (Baarda '67 et ’68) received a widespread acknowledgment and provide guidelines in network design as well as in out- lier identification. Many testing strategies have been sug- gested to improve the efficiency of data snooping and reduce masking effects: some are based on still unidimensional test statistics and look for a satisfactory backward and/or for- ward elimination procedure. In the last decade also ro- bust estimation procedures became part of the mathemat- ical background of photogrammetric and generally survey and mapping community; further achievements are com- ing out in robust testing. This might lead in the future to a decline of the fortune of the least squares principle; at present, nevertheless, robust estimation methods heavy rely on least squares since, as outlined above, their compu- tational scheme is based on iterative least squares adjust- ments. “Robustness is insensitivity against small deviations from assumptions” (Huber ’81): it is looked for an estimator being perhaps less efficient when all model hypothesis are satisfied, but which is still capable, to the contrary, of identifying the kernel of consistent observation. Among 144 model assumption violations, the more understood is per- haps the shape of the true underlying distribution deviating slightly from the assumed (usually the gaussian distribu- tion). According to (Hampel et al. ’86), “robust statis- tics are the statistics of the approximate parametric mod- els”; this means robust estimators are derived under a dis- tributional model more flexible than the maximum likeli- hood estimators: more precisely they provide an infinite di- mensional neighbourhood of a specified parametric model. Contaminations of the basic distribution are explicitly ac- counted for. The estimation procedure is designed to pro- vide a screening among the observations, taking a priori into account that not all of them should be given the same role in determining the solution. This does not happen to least squares estimates, where all observations equally contribute, on the basis of their a priori variance, to the solution. Most robust estimation techniques are basically down- weighting methods where in an iterative least squares scheme suspicious observations undergo to a decrease of their role in determining the solution, through the modifi- cation of their weights according to some specified criterion. The amount of the weight change is generally determined on the basis of the (standardized) residual of the observation and may involve from a theoretical point of view all obser- vations. Following a more pratical approach (Bucciarelli et al. ’92), changes to the weights will be assumed to be signif- International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B1. Vienna 1996 ican! and the « to ei ical chan quer outli ing 1 with Sec tech inst: whe! in re gres: signi obse tion. In | the : dire nity iden Man this mati ular, trian Ro ful v ics v may tion ods) for | Dig intei (e.g. trac reco and who. tion. proc The the the 1 ital emp

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