Michel Morgan

Photogrammetric and Lidar Data Registration Using Linear Features

The enormous increase in the volume of datasets acquired by lidar systems is leading towards their extensive exploitation in a variety of applications, such as, surface reconstruction, city modeling, and generation of perspective views. Though being a fairly new technology, lidar has been influenced by and had a significant impact on photogrammetry. Such an influence or impact can be attributed to the complementary nature of the information provided by the two systems. For example, photogrammetric processing of imagery produces accurate information regarding object space break lines (discontinuities). On the other hand, lidar provides accurate information describing homogeneous physical surfaces. Hence, it proves logical to combine data from the two sensors to arrive at a more robust and complete reconstruction of 3D objects. This paper introduces alternative approaches for the registration of data captured by photogrammetric and lidar systems to a common reference frame. The first approach incorporates lidar features as control for establishing the datum in the photogrammetric bundle adjustment. The second approach starts by manipulating the photogrammetric imagery to produce a 3D model, including a set of linear features along object space discontinuities, relative to an arbitrarily chosen coordinate system. Afterwards, conjugate photogrammetric and lidar straight-line features are used to establish the transformation between the arbitrarily chosen photogrammetric coordinate system and the lidar reference frame. The second approach (bundle adjustment, followed by similarity transformation) is general enough to be applied for the co-registration of multiple three-dimensional datasets regardless of their origin (e.g., adjacent lidar strips, surfaces in GIS databases, and temporal elevation data). The registration procedure would allow for the identification of inconsistencies between the surfaces in question. Such inconsistencies might arise from changes taking place within the object space or inaccurate calibration of the internal characteristics of the lidar and the photogrammetric systems. Therefore, the proposed methodology is useful for change detection and system calibration applications. Experimental results from aerial and terrestrial datasets proved the feasibility of the suggested methodologies.

Automatic calibration of low-cost digital cameras

Recent developments of digital cameras in terms of the size of charge-coupled device (CCD) arrays and reduced costs are leading to their applications in traditional as well as new photogrammetric, surveying, and mapping functions. Digital cameras, intended to replace conventional film-based mapping cameras, are becoming available along with many smaller formats capable of precise measurement applications. All such cameras require careful calibration to determine their metric characteristics, which are essential to carrying out photogrammetric activities. We introduce a new approach for incorporating straight lines in a bundle adjustment for calibrating off-the-shelf, low-cost digital cameras. The optimal configuration for successfully deriving the distortion parameters is considered when establishing the required test field. Moreover, a framework for automatic extraction of the straight lines in the images is presented and tested. The developed calibration procedure can be used as an efficient tool to investigate the most appropriate model that compensates for various distortions associated with the camera being calibrated. Experiments performed to compare line-based with traditional point-based self-calibration methods prove the feasibility of the suggested approach.

Stability Analysis and Geometric Calibration of Off-the-Shelf Digital Cameras

Recent developments of digital cameras in terms of the size of a Charged Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) arrays, as well as reduced costs, are leading to their applications in traditional and new photogrammetric, surveying, and mapping functions. Such cameras require careful calibration to determine their metric characteristics, as defined by the Interior Orientation Parameters (IOP), which are essential for any photogrammetric activity. Moreover, the stability of the estimated IOP of these cameras over short and long time periods has to be analyzed and quantified. This paper outlines the incorporation of straight lines in a bundle adjustment procedure for calibrating off-the-shelf/low-cost digital cameras. A framework for automatic extraction of the straight lines in the images is also presented and tested. In addition, the research introduces new approaches for testing the camera stability, where the degree of similarity between reconstructed bundles using two sets of IOP is quantitatively evaluated. Experimental results with real data proved the feasibility of the line-based selfcalibration approach. Analysis of the estimated IOP from various calibration sessions over long time periods revealed the stability of the implemented camera.

Epipolar Resampling of Space-borne Linear Array Scanner Scenes Using Parallel Projection

Epipolar resampling aims at generating normalized images where conjugate points are located along the same row. Such a characteristic makes normalized imagery important for many applications such as automatic image matching, aerial triangulation, DEM and ortho-photo generation, and stereo-viewing. Traditionally, the input media for the normalization process are digital images captured by frame cameras. These images could be either derived by scanning analog photographs or directly captured by digital cameras. Current digital frame cameras provide smaller format imagery compared to those of analog cameras. In this regard, linear array scanners are emerging as a viable substitute to two-dimensional digital frame cameras. However, linear array scanners have more complex imaging geometry than that of frame cameras. In general, the imaging geometry of linear array scanners produces non-straight epipolar lines. Moreover, epipolar resampling of captured scenes according to the rigorous model, which faithfully describes the imaging process, requires the knowledge of the internal and external sensor characteristics as well as a Digital Elevation Model (DEM) of the object space. Recently, parallel projection has emerged as an alternative model approximating the imaging geometry of high altitude scanners with narrow angular field of view. In contrast to the rigorous model, the parallel projection model does not require the internal or the external characteristics of the imaging system and produces straight epipolar lines. In this paper, the parallel projection equations are modified for better modeling of linear array scanners. The modified parallel projection model is then used to resample linear array scanner scenes according to epipolar geometry. Experimental results using Ikonos and SPOT data demonstrate the feasibility of the proposed methodology.

Quantitative measures for the evaluation of camera stability

Increasing resolution and lower costs of off-the-shelf digital cameras are giving rise to their utilization in traditional and new photogrammetric activities, such as transportation, surveillance, archeological, industrial, and medical applications. This progress is enabling amateur users to generate high-quality photogrammetric products using such cameras. For most, if not all photogrammetric applications, the internal metric characteristics, usually known as the interior orientation parameters (IOP), of the implemented camera must be determined and analyzed. The derivation of these parameters is usually achieved by implementing a bundle adjustment with a self-calibration procedure. The issue of camera stability has been rarely addressed when dealing with analog metric cameras, since they have been carefully designed and built to ensure the utmost stability of their internal characteristics. However, the stability of digital cameras must be investigated, since these cameras are not built with photogrammetric applications in mind. We introduce two quantitative methods for testing camera stability, where the degree of similarity between reconstructed bundles from two sets of IOP is evaluated. The stability of nine amateur and professional digital cameras are checked over 8 months. The experimental result depicts the stability of the majority of these cameras.

AUTOMATIC MATCHING AND THREE-DIMENSIONAL RECONSTRUCTION OF FREE-FORM LINEAR FEATURES FROM STEREO IMAGES

Also,surfaces constitutean importantlayer ofany GIS database. Abstract Withthegrowingavailabilityofhigh-resolutiondigitalcameras, Automatic matching of free-form linear features in overlapping the need for automatic and reliable surface reconstruction from large-scale imagery over urban areas still remains to be a imagery is becoming urgent. Automatic surface reconstruction problem in both the photogrammetric and computer vision from large-scale imagery over urban areas remains an unsolved communities. Although there is a variety of algorithms that problem in spite of the many efforts in the photogrammetric have been developed to solve this problem, reliable results are and computer vision fields. The complexity of the input imag- not always guaranteed. Differences in illumination conditions, ery and the ill-posed characteristics of that problem are among relief displacement, and occlusions are some of the factors themajor obstaclesencountered byresearchers. Thetraditional that make solving the matching problem more challenging. photogrammetric solution for surface reconstruction has three The deficiency of available techniques stems from the fact that basic steps. First, conjugate (matching) entities within overlap- they do not consider the perspective geometry of the imaging ping images are determined. The second step involves the system in the matching process. Moreover, it is usually determinationof therelativeorientation parameters(ROP)relat- assumed that conjugate entities are almost exact copies of ingthe twoimages ofastereo pair.Finally, matchedentitiesare each other (this is rarely the case). The need for a reliable projected into the stereo model using the derived ROP in step 2. algorithm that can handle large-scale imagery over urban Solving the correspondence problem is the most difficult step areas is growing, especially with the increasing availability within the surface reconstruction process. Strategies described of high-resolution aerial imagery. In this research, a new in the photogrammetric and computer vision literature for approach for automatic matching and three-dimensional finding conjugate entities within overlapping images include reconstruction of free-form linear features from stereo images area-based matching, feature-based matching, and relational is proposed. The suggested strategy is based on The Modified matching (Ackermann, 1984; Grimson, 1985; Rosenholm, Iterated Hough Transform (MIHT) for Robust Parameter 1987; Schenk, 1999). Estimation. MIHT relies on the mathematical relationship Automatic matching of distinct points is common in the between conjugate entities (the coplanarity condition when photogrammetric community. It usually starts by searching for dealing with a stereo pair). As a result, it overcomes problems interestingpoints thatsatisfy distinctness,stability,invariance, arising from relief displacements and/or occlusions. Moreover, uniqueness, and interpretability criteria. An extensive body of

Line-Based Modified Iterated Hough Transform for Autonomous Single-Photo Resection

Automatic single photo resection (SPR) remains to be one of the challenging problems in digital photogrammetry. Earlier attempts to automate the space resection task were mainly point-based, where image-point primitives are first extracted and matched with their object counterparts. The matched primitives are then used to estimate the exterior orientation parameters (EOP). However, visibility and uniqueness of distinct control points in the input imagery limit robust automation of the pose estimation procedure. Recent advances in digital photogrammetry mandate adopting higher-level primitives such as control linear features replacing traditional control points. Linear features can be automatically extracted in image space. On the other hand. object-space control linear features can he extracted from an existing GIS layer containing 3D vector data such as a road network and/or terrestrial mobile mapping systems (MMS). In this paper, we present a line-based approach for simultaneously determining the position and attitude of the involved imagery as well as establishing the correspondence between image- and object-space features. This approach is motivated by the fact that captured imagery over a man-made environment is rich in straight-line segments. Moreover, free-form linear features can be reliably represented with sufficient accuracy by a sequence of straight-line segments (i.e., polylines). The suggested methodology starts by establishing a general mathematical model for relating conjugate straight-line segments to the EOP of the image under consideration. Then, a Modified Iterated Hough Transform (MIHT) strategy is adopted to derive the correspondence between image and object primitives as well as the position and the attitude of the involved imagery. This approach does not necessitate having one-to-one correspondence between image- and object-space primitives. which makes it robust against changes and/or discrepancies between the primitives. The parameter estimation and matching processes follow an optimum sequential procedure, which depends on the sensitivity of the mathematical model, relating corresponding primitives with different orientation at various image regions, to incremental changes in the EOP. Experimental results using real data proved the feasibility and robustness of the proposed approach even in the presence of a large percentage of outliers and/or discrepancies between the image- and object-space features.

Comprehensive comparisons among alternative sensor models for high resolution satellite imagery

Geometric modeling of satellite imagery is a prerequisite for many mapping and GIS applications. The more valid the sensor modeling is, the more accurate the end products are. Two main categories of sensor models exist; rigorous and approximate modeling. The former resembles the true geometry of the image formation procedure. Such a modeling requires the availability of the internal and external characteristics of the camera, which might not be always available. In addition, if these parameters are negatively affected by bias values, the accuracy of the rigorous model becomes questionable. Recently, there has been an increasing interest in approximate models, as they do not require the internal or external characteristics of the sensor. In this paper, a comparison between the rigorous and different approximate models is presented. Experimental results show the sensitivity of the rigorous model to bias values. Using an IKONOS dataset, it was found that the modified parallel projection model performs the best among all approximate models using a small number of control points. KeywordsSatellite Imagery; Rigorous Modeling; Approximate Modeling; Interior Orientation Parameters; Bias