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Data Acquisition Photogrammetric Applications
Data Acquisition, Automation C HRISTIAN H EIPKE Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, Germany Synonyms Automatic information extraction; Image analysis; Scene analysis; Photogrammetry Definition Automatic data acquisition is the extraction of information from images, relevant for a given application, by means of a computer. Photogrammetric image processing is divided into two aspects, i. e., the geometric/ radiometric image evaluation and image analysis. Geometric/radiometric image evaluation comprises image orientation, the derivation of geometric surface descriptions and orthoprojection. Image analysis contains the extraction and description of three-dimensional (3D) objects. A strict separation of both areas is possible neither for manual nor for automatic photogrammetric image processing. Historical Background In the past, geometric/radiometric image evaluation and image analysis were two clearly separated steps in the photogrammetric processing chain. Using analogue imagery, automation was understood as a supporting measure for a human operator, e. g., by driving the cursor automatically to a predefined position in image and/or object space to capture well-defined tie points or to speed up image coordinate measurement of ground control points or digital terrain model (DTM) posts. The first successful attempts
towards a more elaborate role for the computer became commonplace once analogue images could be scanned and subsequently processed in digital form. In this way, interior and relative orientations, as well as large parts of aerial triangulation and DTM generation, became candidates for a fully automatic work flow. The recent development of digital aerial cameras inspires hope for further automation in the image analysis step. Scientific Fundamentals When using digitized or digitally acquired images, the border between geometric/radiometric image evaluation and image analysis becomes blurred, mostly because, due to automation, the formerly decisive manual measurement effort has lost much of its significance. Therefore, already in the orientation phase a point density can be used, which is sufficient for some digital surface models (DSMs). Methods for the integrated determination of image orientation, DSMs, and orthophotos have been known for some time, but for the sake of clarity the various steps shall be looked at separately here. The components of image orientation are the sensor model, i. e., the mathematical transformation between image space and object space, and the determination of homologous image primitives (mostly image points). As far as the sensor model is concerned, the central projection as a classical standard case in photogrammetry must be distinguished from line geometry. In the context of bundle adjustment the central projection is traditionally described by means of collinearity equations. It should be noted, however, that the resulting set of equations is nonlinear in the unknown parameter
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