Fig. 9.14 DSM (left column) and filtered object points (right column) obtained from skewness balancing of two different LIDAR tiles. Top: Thamesmead, London, UK (courtesy of Environment Agency, UK). Bottom: Mannheim, Germany (courtesy of TopoSys GmbH and the Stadt Mannheim, Germany)

9.5 Research Challenges

With the development of remote sensing technology and advanced signal processing algorithms, 3D DEM generation from remote sensing has demonstrated enormous potential with advantages of automation, economy, and large coverage of terrain. Related techniques have been maturing due to significant investment in sensing devices, instruments, processing algorithms, and software development. The launch of Earth observation satellites, such as, Envisat, Landsat, GeoEye, and Worldview has brought opportunities as well as challenges to the research communities.

•When the high accuracy, high resolution, and high density data are continuously acquired from various Earth observation satellites, huge datasets are generated. How to store and retrieve these datasets is the first challenge. These may require reliable servers and tangible databases for data storage, and robust algorithms for information retrieval from the databases if needed.

•For DEM generation from stereoscopic imagery, further improving DEM accuracy requires better mathematical models which can adaptively correct errors caused by sensors’ platform attitude instability and geometric distortion of images. Automation demands more robust algorithms for image matching processes.

•As described previously, most current techniques used in 3D DEM generation from remote sensing need intensive interaction from operators. They are timeconsuming and the accuracy of the final product is difficult to control. This demands researchers’ further understanding of physical properties of sensing elements (e.g. SAR) with accurate theoretical models for signal interpretation, and better knowledge of satellite orbits and imaging geometry (for both stereo images and SAR images).

•For LIDAR, power consumption is the key issue for spaceborne missions although lasers can provide more accurate elevation data compared with other devices. Quantitative analysis of the effects caused by weather and atmospheric conditions to LIDAR is required to ensure that the raw signal/data is interpreted correctly.

The above aspects need to be addressed in order to bring more automation and higher accuracy to the DEMs generated from remote sensing.

9.6 Concluding Remarks

In this chapter, three methods used for 3D DEM generation from remote sensing have been introduced. These are DEM generation from optical stereoscopic imagery, InSAR, and airborne LIDAR. All of these have shown enormous potential in many application areas. After working through this chapter, it is expected that readers appreciate the achievements and are aware of the problems remaining in this research area. Readers should have sufficient knowledge to answer the questions presented in Sect. 9.8 and carry out the exercises in Sect. 9.9, with the comprehensive references listed at the end of this chapter. Also, readers should be able to do the following.

•Outline processing procedures of DEM generation from satellite stereoscopic imagery, InSAR, and airborne LIDAR.

•Explain how the 3D passive vision technology developed in the computer vision community (Chap. 2) can be applied to DEM generation from satellite stereoscopic imagery.

•Explain how the 3D active vision technology can be used for DEM generation from InSAR and LIDAR.

•Appreciate various algorithms developed in the area of DEM generation for stereo-matching, image registration, phase unwrapping, and data filtering.

•Estimate DEM accuracy based on knowledge of imaging geometry and relative physical properties of sensing devices.

•Identify advantages and disadvantages for DEM generation from different remote sensing approaches.

•Perform DEM generation with available data (possibly through the use of commercial software packages).

9.7 Further Reading

The following books are recommended for the purposes of broadening knowledge in the field, from instruments for raw data acquisition to DEM applications.

1.James B. Campbell (1996), Introduction to Remote Sensing, 2nd edition, Taylor & Francis Ltd—This book provides an introduction level of remote sensing including image acquisition, analysis, and applications. Both passive imaging and active radar are discussed. Spaceborne as well as airborne remote sensing techniques are covered.

2.Peter Burrough, and Rachael A. McDonnell (1998), Principles of Geographical Information Systems, Oxford University Press. Readers may access this book to gain knowledge of spatial science and geographical information systems (GIS).

3.Chris Oliver and Shaun Quegan (1998), Understanding Synthetic Aperture Radar Images, Artech House Inc. This book describes insight of SAR and SAR image analysis and processing. It helps readers gain knowledge of SAR principles and image formation.

4.Ramon F. Hanssen (2001), Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic Publishers. This book is specifically dedicated to InSAR. It details fundamentals of radar system theory, interferometric processing, functional models for InSAR, data analysis and error estimation.

5.Norbert Pfeifer and Gottfried Mandlburger (2009), Chap. 11—LIDAR data filtering and DTM generation, Topographic Laser Ranging and Scanning—Principles and Processing, Edited by Jie Shan and Charles K. Toth, CRC Press. This book explores LIDAR’s potentials and capabilities in topographic mapping. Chapter 11 describes DEM/DTM generation from LIDAR.

9.8 Questions

1.Outline the procedure of DEM generation from satellite stereoscopic image pairs, and indicate possible advantages of using satellite imagery (relative to the use of airborne imagery) in DEM generation.

2.Briefly discuss the physical principle of InSAR in DEM generation, and outline the processing stages of DEM generation from satellite InSAR.

3.Explain possible issues involved in the processes stated in Q. (2), and explain the related solutions.

4.Compare and contrast DEM generation methods of satellite stereoscopic imagery and InSAR, consider aspects of sensor type, resolution, scale, orbit, timing, wavelengths used, and satellite altitude. You may need additional information from the further reading materials to answer this question.

5.Explain LIDAR operation in DEM/DTM generation and outline the key steps to generate the final DEM/DTM from the raw LIDAR point cloud.

9.9 Exercises

1.Simulate an along-track satellite stereoscopic imaging geometry and establish the geometric model of the prospective projection based on the setting illustrated in Fig. 9.2(left).

2.For the across-track satellite stereoscopic imaging shown in Fig. 9.2(right), derive the mathematical model of the perspective projection geometry associated with the cameras and the terrain surface and speculate possible errors which affect the accuracy of final DEMs generated from this setting.

3.Write an essay which discusses state-of-the-art stereo image matching algorithms specifically used for DEM generation from satellite stereoscopic imagery. In preparation of this essay, you may look for image matching algorithms used in commercial DEM generation software products.

4.Based on the discussion in Sect. 9.3.1.2, derive the phase unwrapping algorithms of branch-cuts and least squares.

5.Earth observation based on satellite missions normally is very expensive so that it requires resources of a national government to fund the related projects. Many people question whether it is necessary to spend government funds for such programs. Write an essay, with your argument, to justify the costs. You may choose positive or negative views.

6.Compare LIDAR filtering techniques introduced in this chapter and implement them in your choice of programming language.

7.Implement the Skewness Balancing algorithm in a programming language of your choice, and apply it to a LIDAR point cloud data set to generate a DEM. You may access the LIDAR data at http://www.commission3.isprs.org/wg4/.

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