Chapter 9
3D Digital Elevation Model Generation
Hong Wei and Marc Bartels
Abstract This chapter presents techniques used for the generation of 3D digital elevation models (DEMs) from remotely sensed data. Three methods are explored and discussed—optical stereoscopic imagery, Interferometric Synthetic Aperture Radar (InSAR), and LIght Detection and Ranging (LIDAR). For each approach, the state-of-the-art presented in the literature is reviewed. Techniques involved in DEM generation are presented with accuracy evaluation. Results of DEMs reconstructed from remotely sensed data are illustrated. While the processes of DEM generation from satellite stereoscopic imagery represents a good example of passive, multi-view imaging technology, discussed in Chap. 2 of this book, InSAR and LIDAR use different principles to acquire 3D information. With regard to InSAR and LIDAR, detailed discussions are conducted in order to convey the fundamentals of both technologies.
9.1 Introduction
A digital elevation model (DEM) is a digital representation of a terrain’s surface, created from terrain elevation data, with horizontal coordinates X and Y , and altitude Z. The methods used for DEM generation are roughly categorized as follows.
•DEM generation by passive remote sensors relying on natural energy sources like the sun. These could be airborne or spaceborne multispectral/panchromatic images evaluated as stereo-pairs to extract 3D information. This is also referred to as classical photogrammetry.
•DEM generation by active remote sensors, which sense artificial energy sources deliberately transmitted to a target. These include radar (Radio Detection And
H.Wei ( ) · M. Bartels
Computational Vision Group, School of Systems Engineering, University of Reading, Reading RG6 6AY, UK
e-mail: h.wei@reading.ac.uk
M. Bartels
e-mail: marc.bartels.berlin@gmail.com
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DOI 10.1007/978-1-4471-4063-4_9, © Springer-Verlag London 2012 |
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Ranging) stereo-pairs, InSAR (Interferometric Synthetic Aperture Radar), and LIDAR (LIght Detection And Ranging), which involves laser scanning.
•DEM generation by geodetic measurements. Geodetic instruments, which are integrated devices measuring lengths, angles, and levels of land surface, collect the
measured coordinates (X, Y ) and altitude (Z) point by point in a designated coordinate system (e.g. WGS841). With these data, a topographic map of the area is made with contour lines. Digitizing these contour lines and gridding them if needed, turns the hardcopy map to digital data format (i.e. DEM). This is a traditional method.
Techniques discussed in this chapter are mainly concerned with DEM generation from remote sensing, and cover both passive and active approaches. Three key remote sensing methods for DEM generation are discussed as optical stereo imaging, InSAR, and LIDAR. Although optical stereo images and InSAR data can be acquired from both airborne and spaceborne remote sensing, only spaceborne is considered in the discussion, whereas for an airborne representative, LIDAR is presented. A brief literature review of these three techniques in DEM generation is given in each of Sects. 9.2, 9.3, and 9.4, respectively.
There are three relevant technical terms that we need to distinguish: DEM, DTM (Digital Terrain Model), and DSM (Digital Surface Model). A DEM is a general term referring to an elevation model; a DSM is a surface model representing sensor detected height including all visible objects on the top of a surface; and a DTM is a terrain model showing bare ground surface topography. In using spaceborne stereoscopic imagery and InSAR to generate DEMs, there is hardly any difference between DSM and DTM due to the typical elevation accuracy of ±10 meters [38, 73], especially in rural areas where buildings and other man-made objects are absent from remotely sensed data. Under this circumstance, the term DEM is generally used to represent the 3D reconstruction in the photogrammetry and remote sensing community. However, with airborne LIDAR, the vertical accuracy and the capability to capture multiple returns (or echoes) necessitate the extension of the term DEM to the terms of DSM and DTM [143]. It is possible to remove objects and generate a more accurate DTM from the original DSM.
For many reasons, it has been a longstanding goal of mankind to be able to look at the Earth and find out what its surface or terrain looks like. With the development of remote sensing technologies and image interpretation techniques, it has become possible to see the Earth surface from a bird’s eye view, as shown in Fig. 9.1. Such data has been used to monitor changes in the Earth’s surface as a function of time, especially after natural disasters, such as volcanoes, earthquakes and tsunamis. It has also been used to predict land erosion and slides. In these applications, DEMs play an important role to recover the surface topography. Associated with co-registered satellite images, a vivid land surface can be displayed for visualization. Apart from visualization, DEMs are also required by many disciplines of scientific research involving studies of the Earth’s land surface, such as cartography, climate modeling,
1WGS84: World Geodetic Systems dating from 1984 and last revised in 2004.
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Fig. 9.1 Bird’s eye view of a mountainous area from a 3D DEM mapped with texture from a satellite image. Copyright METI/NASA, reprinted with permission from ERSDAC (Earth Remote Sensing Data Analysis Center), http://www.ersdac.or.jp/GDEM/E/2.html
biogeography, geology, and soil science [80]. With the development of new remote sensing technologies with which data accuracy has dramatically improved, DEMs are also being used within an increasingly wide range of applications. These include site suitability studies for urban developments, telecommunications base stations and intelligent transportation systems, floodplain mapping and land erosion analysis, and applications in agriculture and forestry [105].
Chapter Outline After this introduction, the chapter is organized in the following main sections.
•Section 9.2 introduces DEM generation from stereoscopic imagery. In this section, a detailed literature review is presented on the technique, followed by accuracy evaluation. A step-by-step guide is given in an example of DEM generation from stereoscopic imagery.
•Section 9.3 discusses the principle and techniques for DEM generation from InSAR and accuracy analysis is conducted with regards to error sources. Examples of DEM generation from InSAR show the detailed process.
•Section 9.4 details DEM generation from LIDAR. It covers LIDAR data acquisition, data types and accuracy, and LIDAR interpolation. Comprehensive literature review is given for LIDAR filtering, an important step towards an accurate DEM (or DTM). The algorithm of Skewness Balancing in DEM generation from LIDAR is presented in detail.
•Section 9.5 discusses the research challenges remaining in the area of 3D DEM generation from remote sensing.
•Section 9.6 summarizes the chapter and lists clear learning targets for readers to achieve.
•Section 9.7 gives additional reading material for readers to further expand their knowledge in the areas of remote sensing, geographical information systems, and InSAR and LIDAR techniques.