Clive S. Fraser

Digital camera self-calibration

Abstract Over the 25 years since the introduction of analytical camera self-calibration there has been a revolution in close-range photogrammetric image acquisition systems. High-resolution, large-area ‘digital’ CCD sensors have all but replaced film cameras. Throughout the period of this transition, self-calibration models have remained essentially unchanged. This paper reviews the application of analytical self-calibration to digital cameras. Computer vision perspectives are touched upon, the quality of self-calibration is discussed, and an overview is given of each of the four main sources of departures from collinearity in CCD cameras. Practical issues are also addressed and experimental results are used to highlight important characteristics of digital camera self-calibration.

Bias Compensation in Rational Functions for Ikonos Satellite Imagery

A method for the removal of exterior orientation biases in rational function coefficients (RPCs) for Ikonos imagery is developed. These biases, which are inherent in RPCs derived without the aid of ground control, give rise to geopositioning errors. The 3D positioning error can subsequently be compensated during spatial intersection by two additional parameters in image space that effect a translation of image coordinates. The resulting bias parameters can then be used to correct the RPCs supplied with Ikonos Geo imagery such that a practical means is provided for bias-free ground point determination, nominally to meter-level absolute accuracy, using entirely standard procedures on any photogrammetric workstation that supports Ikonos RPCs. The method requires provision of one or more ground control points. Aside from developing the bias compensation method, the paper also summarizes practical testing with bias-corrected RPCs that has demonstrated sub-meter geopositioning accuracy from Ikonos Geo imagery.

Bias-compensated RPCs for Sensor Orientation of High-resolution Satellite Imagery

The demand for higher quality metric products from high-resolution satellite imagery (HRSI) is growing, and the number of HRSI sensors and product options is increasing. There is a greater need to fully understand the potential and indeed shortcomings of alternative photogrammetric sensor orientation models for HRSI. To date, rational functions have proven to be a viable alternative model for geo-positioning, and with the recent innovation of bias-compensated RPC bundle adjustment, it has been demonstrated that sensor orientation to sub-pixel level can be achieved with minimal ground control. Questions have lingered, however, as to the general suitability of bias-compensated rational polynomial coefficients (RPCs), and indeed rational functions in general. The purpose of this paper is to demonstrate the wide applicability of bias-compensated RPCs for high-accuracy geopositioning from stereo HRSI. The case of stereo imagery over mountainous terrain will be specifically addressed, and results of experimental testing of both Ikonos and QuickBird imagery will be presented.

Processing of Ikonos imagery for submetre 3D positioning and building extraction

Abstract An investigation of the application of 1-m Ikonos satellite imagery to 3D point positioning and building reconstruction is reported. The focus of the evaluation of Geo panchromatic imagery has been upon 3D positioning accuracy, radiometric quality and attributes of the image data for building feature extraction. Following an initial review of characteristics of the Ikonos system, the multi-image dataset employed is described, as is the Melbourne Ikonos testfield. Radiometric quality and image preprocessing aspects are discussed, with attention being given to noise level and artifacts, as well as to methods of image enhancement. The results of 2D and 3D metric accuracy tests using straightforward geometric sensor models are summarised, these showing that planimetric accuracy of 0.3–0.6 m and height accuracy of 0.5–0.9 m are readily achievable with only three to six ground control points. A quantitative and qualitative assessment of the use of stereo Ikonos imagery for generating building models is then provided, using the campus of the University of Melbourne as an evaluation site. The results of this assessment are discussed, these highlighting the high accuracy potential of Ikonos Geo imagery and limitations to be considered in building reconstruction when a comprehensive and detailed modelling is required.

Monitoring the thermal deformation of steel beams via vision metrology

Abstract The multi-epoch deformation monitoring of a series of super-hot steel beams by digital close-range photogrammetry is reported. An on-line configuration of three CCD cameras was established to measure both stable reference points and targets subject to positional displacement. Measurements for each beam were conducted at 70–80 epochs over 2 h as the steel cooled from 1100°C to near room temperature. Special targeting was required to accommodate the changing colour of the beams from white-hot to brown as they cooled and ensure target survival through a large temperature range. A computational approach was employed whereby the photogrammetric triangulation process for any given recording epoch utilised all images obtained up until that time. The paper discusses all aspects of the project in which seven beams were monitored to a dimensional tolerance of close to 1 mm (RMS 1-sigma).

AUTOMATIC CAMERA CALIBRATION IN CLOSE-RANGE PHOTOGRAMMETRY

Automatic camera calibration via self-calibration with the aid of coded targets is now very much the norm in closerange photogrammetry. This is irrespective of whether the cameras to be calibrated are high-end metric, or the digital SLRs and consumer-grade models that are increasingly being employed for image-based 3D measurement. Automation has greatly simplified the calibration task, but there are real prospects that important camera calibration issues may be overlooked in what has become an almost black-box operation. This paper discusses the impact of a number of such issues, some of which relate to the functional model adopted for self-calibration, and others to practical aspects which need to be taken into account when pursuing optimal calibration accuracy and integrity. Issues discussed include interior orientation stability, calibration reliability, focal plane distortion, image point distribution, variation in lens distortion with image scale, colour imagery and chromatic aberration, and whether 3D object space control is warranted. By appreciating and accounting for these issues, users of automatic camera calibration will enhance the prospect of achieving an optimal recovery of scene-independent camera calibration parameters.

Design and implementation of a computational processing system for off-line digital close-range photogrammetry

Through the adoption of recent innovations in automation in vision metrology, it can be demonstrated that rigorous, yet user-friendly digital photogrammetric processes of calibration and orientation/triangulation can be incorporated into a computational scheme which is on the one hand capable of meeting the demands of high metric quality, while on the other offering the facilities necessary to support wider application by non-specialist users. The software system Australis, developed for image mensuration and restitution of off-line close-range photogrammetric networks, is featured to illustrate these processes and procedures. By describing the structure and components of Australis, the authors aim to demonstrate that many processes which have on occasion been viewed to be the exclusive province of automated, high-precision vision metrology are indeed suited to more general application across a broad range of fields which involve 3D object recording via close-range imagery.

Photogrammetric Camera Component Calibration: A Review of Analytical Techniques

As the field of computer vision advances, more and more applications are calling for enhanced metric performance. Central to any consideration of metric accuracy is the subject of camera system calibration. Analytical camera calibration techniques developed over the past four decades in photogrammetry are very applicable to the video cameras used in today’s machine vision systems. This chapter reviews modern analytical camera calibration techniques employed by photogrammetrists and discusses the potential of these methods for video cameras (principally CCD cameras). Analytical restitution is briefly overviewed, the parameterization of the calibration components is reviewed, and the methods of parameter determination are discussed. The use of photogrammetric calibration techniques offers a means of significantly improving the accuracy of spatial position and orientation information in computer and machine vision systems.

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a “building mask”. The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m2. Thus, the proposed method can be exploited in various applications.

Zoom-Dependent Camera Calibration in Digital Close-Range Photogrammetry

One of the well-known constraints applying to the adoption of consumer-grade digital cameras for photogrammetric measurement is the requirement to record imagery at fixed zoom and focus settings. The camera is then calibrated for the lens setting employed. This requirement arises because calibration parameters vary significantly with zoom/focus setting. In this paper, a zoom-dependent calibration process is proposed whereby the image coordinate correction model for interior orientation and lens distortion is expressed as a function of the focal length written to the EXIF header of the image file. The proposed approach frees the practitioner from the requirement to utilize fixed zoom/focus settings for the images forming the photogrammetric network. Following a review of the behavior of camera calibration parameters with varying zoom settings, an account of the newly developed zoom-dependent calibration model is presented. Experimental results of its application to four digital cameras are analysed. These show that the proposed approach is suited to numerous applications of medium-accuracy, digital, close-range photogrammetry.