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Automated DEM Extraction in Digital Aerial Photogrammetry: Precisions and Validation for Mass Movement Monitoring

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Automated DEM Extraction in Digital Aerial Photogrammetry: Precisions and Validation for Mass Movement Monitoring View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Earth-prints Repository ANNALS OF GEOPHYSICS, VOL. 48, N. 6, December 2005 Automated DEM extraction in digital aerial photogrammetry: precisions and validation for mass movement monitoring Massimo Fabris (1) and Arianna Pesci (2) (1) Dipartimento di Fisica, Settore Geofisica, Università degli Studi di Bologna, Italy (2) Istituto Nazionale di Geofisica e Vulcanologia, Sede di Bologna, Italy Abstract Automated procedures for photogrammetric image processing and Digital Elevation Models (DEM) extraction yield high precision terrain models in a short time, reducing manual editing; their accuracy is strictly related to image quality and terrain features. After an analysis of the performance of the Digital Photogrammetric Work- station (DPW) 770 Helava, the paper compares DEMs derived from different surveys and registered in the same reference system. In the case of stable area, the distribution of height residuals, their mean and standard devia- tion values, indicate that the theoretical accuracy is achievable automatically when terrain is characterized by regular morphology. Steep slopes, corrugated surfaces, vegetation and shadows can degrade results even if man- ual editing procedures are applied. The comparison of multi-temporal DEMs on unstable areas allows the mon- itoring of surface deformation and morphological changes. Key words digital photogrammetry – automatic residuals to identify deforming areas, estimate processing – DEM – precision – monitoring mass movement and physical surface changes. Co-registration is a crucial operation which depends on the correct choice and measurement 1. Introduction of control points (the final results could be de- graded by systematic errors). Digital Elevation Models (DEMs) play an Several techniques are suitable for DEM ex- important role in Earth surface deformation stud- traction like digital aerial and terrestrial pho- ies: eruptive events, gravitative instabilities, land- togrammetry, airborne and terrestrial laser slides and glacier evolution, geomorphological scanning, GPS methodology with its different variations and crustal deformations can be detect- measurement approaches and active and pas- ed and evaluated by means of multi-temporal sive remote sensing, with optical satellite im- DEM comparisons (Kaab and Funk, 1999; Baldi agery systems (Fraser et al., 2002). et al., 2002; Honda and Nagai, 2002; Mora et al., In particular, digital aerial photogrammetry 2003; van Westen and Lulie Getahun, 2003). is a powerful tool in surface model generation Models registered in a common reference extracting high resolution DEMs by means of system can be directly compared defining height automated image matching procedures (Farrow and Murray, 1992; Heipke, 1995; Kraus, 1998; Mitchell and Chadwick, 1999; Schenk, 1999). Several institutes and agencies planned and executed photogrammetric flights over Italian Mailing address: Dr. Arianna Pesci, Istituto Nazionale di Geofisica e Vulcanologia, Sede di Bologna,Via D. Creti regions providing a wide historical data set: in 12, 40128 Bologna, Italy; e-mail: [email protected] particular, in 1954, 1976 and 1998 an almost 973 Massimo Fabris and Arianna Pesci total coverage of the peninsula territory was the position of an object can be described in a de- achieved. fined reference frame. The processing of stereo Even if many processing steps, starting digital images can be automated using image from image orientation (Hellwick et al., 1994; matching procedures, based on well defined Tang and Heipke, 1996; Heipke, 1997; Dow- techniques relative to shape or grey/colour inten- man, 1998;) to DEM extraction (Ackermann, sity for the same zones (Kraus, 1998). Generally, 1996; Gasior, 1996; Bacher, 1998; Karras et al., matching algorithms are classified into three 1998) are automated, the final ‘check’ still de- principal groups: the ABM (Area Based Match- pends on the operator’s stereoscopic vision: ing), the FBM (Feature Based Matching) and the manual editing is required to correct correlation RM (Relational Matching) (Forstner, 1986; errors where several points do not fit the ground. Shapiro and Haralick, 1987; Hannah, 1989; Cho, At present many acquisitions are performed 1995; Heipke, 1996; Kraus, 1997; Wang, 1998). using digital cameras providing high resolution In ABM small windows composed of grey val- images directly, while in the frame of digital ues are used as matching primitives to perform processing, historical images (film photo- comparisons over homologous zones of images graphs) acquired by means of analogic cameras until the best correspondence is reached: cross have to be converted into a digital format using correlation or Least Square Matching (LSM) high quality photogrammetric scanners. methods can be used. DEM internal precision and grid dimension In FBM, features (points, lines, edge ele- are strictly related to the quality of metric camera, ments, small regions, or other elements) are ex- photographs scale, scanner efficiency, resolution tracted in each image individually prior to at ground, view angles, surface morphology, veg- matching them: the search is performed in terms etation, shadows and atmospheric conditions. of coordinates relative to the same objects char- In this paper, different automated proce- acterized by a set of attributes (length, size, cur- dures are analysed and compared to provide ac- vature, average brightness for regions, etc.). curate DEMs in the shortest time, reducing The RM concerns global features that are manual interactions in the frame of the Digital composed of different local features: the rela- Photogrammetric Workstation DPW 770 Hela- tions can be geometric (angle, distance, etc.) or va (Dowman, 1990; Dowman et al., 1992). radiometric (grey level between two adjacent Several applications were performed to de- regions, etc.). fine the realistic DEM precision, estimating Digital photogrammetry is able to acquire threshold values for movement detection on many 3D points for high spatial resolution different area typologies. DEM generation through semi-automatic pro- cedures, overcoming the problems of process- ing time and costs of the analytical approach. 2. Aerial photogrammetry overview The accuracy of digital photogrammetry mainly depends on the camera-object distance, The photogrammetric technique defines image quality and resolution; the latter is relat- shape, size and position of objects using images ed to the digital camera characteristics or to the taken from different points of view. Images can scanner resolution when the ‘analogic’ one is be acquired by analogic or digital cameras; be- used. Dealing with terrestrial applications, cause digital photogrammetry processes nu- where the camera-object distances are short meric images, the available film frames must (from few to some hundred meters), the preci- first be digitized and translated from a continu- sion of extracted DEMs ranges from millimet- ous to a discrete data set. At the same time the ric to few centimetres. In the case of aerial sur- intensity of the signal is given by a grey or veys, the image scale may range from 1:1000 to colour scale assigned to each pixel. 1:50000 or more; the precisions decrease from In general, using the metric properties of an few centimetres to several meters (Kraus, image, together with the coordinates of several 1998). Obviously, the image correlation method Ground Control Points (GCP), the geometry and adopted for the DEM automatic extraction 974 Automated DEM extraction in digital aerial photogrammetry: precisions and validation for mass movement monitoring Fig. 1. Aerial digital image at 1:17000 scale (Northern Apennine area): four fiducial markers are visible in the corners; the ground control points are uniformly distributed over the overlapping zone. plays a key role in the minimization of errors of tion. In the first case the aim is to create the the photogrammetric working process. stereoscopic model in an arbitrary relative system The standard procedures to generate DEMs using points common to both images (tie points). are based on fundamental steps which consist in The absolute orientation is the transforma- internal orientation, external orientation (regis- tion of the generated stereoscopic model into an tration into a defined reference system) and external reference frame (for instance UTM) point extraction. defined by the coordinates of several ground The internal orientation is performed to de- control points, recognized on the images (fig. fine the frame position inside the camera, using 1); an s-transformation provides the registration the camera calibration certificate (provided by of the two systems. the factory) to correct for distorsion and to as- The aim of this work is to analyse the auto- sign known coordinate values at specific points, mated procedures for DEM extraction using the that is fiducial markers (fig. 1). Digital Photogrammetric Workstation DPW The external orientation is obtained by two 770 Helava, and to validate their internal and subsequent steps: relative and absolute orienta- absolute accuracy. 975 Massimo Fabris and Arianna Pesci 3. The Helava system the three fundamental processing steps (internal orientation, external orientation and point extrac- The Digital Photogrammetric Workstation tion) are executed and DEMs are extracted using DPW 770 Helava processes aerial photographs algorithms especially designed for different mor- or other digital images to obtain maps,
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