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Titlebook: Geomatic Methods for the Analysis of Data in the Earth Sciences; Athanasios Dermanis,Armin Grün,Fernando Sansò Book 2000 Springer-Verlag B

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書(shū)目名稱(chēng)Geomatic Methods for the Analysis of Data in the Earth Sciences
編輯Athanasios Dermanis,Armin Grün,Fernando Sansò
視頻videohttp://file.papertrans.cn/384/383415/383415.mp4
概述Bridging different disciplines.Complete coverage of the topic.Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Earth Sciences
圖書(shū)封面Titlebook: Geomatic Methods for the Analysis of Data in the Earth Sciences;  Athanasios Dermanis,Armin Grün,Fernando Sansò Book 2000 Springer-Verlag B
描述Geomatics is an amalgam of methods, algorithms and practices in handling data referred to the Earth by informatic tools. This book is an attempt to identify and rationally organize the statistical-mathematical methods which are common in many fields where geomatics is applied, like geodesy, geophysics and, in particular, the field of inverse problems and image analysis as it enters into photogrammetry and remote sensing..These lecture notes aim at creating a bridge between people working in different disciplines and making them aware of a common methodological basis.
出版日期Book 2000
關(guān)鍵詞Bildanalyse; Geod?sie; Photogrammetrie; Statistische Methoden; geodesy; geophysics; geoscience; inverse Pro
版次1
doihttps://doi.org/10.1007/3-540-45597-3
isbn_softcover978-3-540-67476-4
isbn_ebook978-3-540-45597-4Series ISSN 0930-0317 Series E-ISSN 1613-2580
issn_series 0930-0317
copyrightSpringer-Verlag Berlin Heidelberg 2000
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Linear and Nonlinear Inverse Problems,ans for computing the data values given a model. This is called the “forward problem”, see figure 1. In the inverse problem, the aim is to reconstruct the model from a set of measurements. In the ideal case, an exact theory exists that prescribes how the data should be transformed in order to reprod
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An overview of data analysis methods in geomatics,Every applied science is involved in some sort of data analysis, where the examination and further processing of the outcomes of observations leads to answers about some characteristics of the physical reality.
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Hermann Weidenfeller,Anton Vlcekes for a signal dependent noise variance function and a method to transform the image, to achieve an image with signal independent noise. Establishing significance tests and the fusion of different channels for extracting linear features is shown to be simplified.
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