Eui-Myoung Kim

New Methodologies for True Orthophoto Generation

Orthophoto production aims at the elimination of sensor tilt and terrain relief effects from captured perspective imagery. Uniform scale and the absence of relief displacement in orthophotos make them an important component of GIS databases, where the user can directly determine geographic locations, measure distances, compute areas, and derive other useful information about the area in question. Differential rectification has been traditionally used for orthophoto generation. For large scale imagery over urban areas, differential rectification produces serious artifacts in the form of double mapped areas at object space locations with sudden relief variations, e.g., in the vicinity of buildings. Such artifacts are removed through true orthophoto generation methodologies which are based on the identification of occluded portions of the object space in the involved imagery. Existing methodologies suffer from several problems such as their sensitivity to the sampling interval of the digital surface model (DSM) as it relates to the ground sampling distance (GSD) of the imaging sensor. Moreover, current methodologies rely on the availability of a digital building model (DBM), which requires an additional and expensive pre-processing. This paper presents new methodologies for true orthophoto generation while circumventing the problems associated with existing techniques. The feasibility and performance of the suggested techniques are verified through experimental results with simulated and real data.

Comprehensive Analysis of Sensor Modeling Alternatives for High Resolution Imaging Satellites

High-resolution imaging satellites are a valuable and cost effective data acquisition tool for a variety of mapping and GIS applications such as topographic mapping, map updating, orthophoto generation, environmental monitoring, and change detection. Sensor modeling that describes the mathematical relationship between corresponding scene and object coordinates is a prerequisite procedure prior to manipulating the acquired imagery from such systems for mapping purposes. Rigorous and approximate sensor models are the two alternatives for describing the mathematics of the involved imaging process. The former explicitly involves the internal and external characteristics of the imaging sensor to faithfully represent the geometry of the scene formation. On the other hand, approximate modeling can be divided into two categories. The first category simplifies the rigorous model after making some assumptions about the system’s trajectory and/or object space. Gupta and Hartley’s model, parallel projection, self-calibrating direct linear transformation, and modified parallel projection are examples of this category. Other approximate models are based on empirical formulation of the scene-to-ground mathematical relationship. This category includes among others, the well-known Rational Function Model (RFM). This paper addresses several aspects of sensor modeling. Namely, it deals with the expected accuracy from rigorous modeling of imaging satellites as it relates to the number of available ground control points, comparative analysis of approximate and rigorous sensor models, robustness of the reconstruction process against biases in the available sensor characteristics, and impact of incorporating multi-source imagery in a single triangulation mechanism. Following a brief theoretical background, these issues will be presented through experimental results from real datasets captured by satellite and aerial imaging platforms.

Image georeferencing using LIDAR data

LIDAR technology is increasingly becoming an industry-standard tool for collecting high resolution data about physical surfaces. LIDAR is characterized by directly collecting numerical 3D coordinates of object space points. Still, the discrete and positional nature of LIDAR datasets makes it difficult to derive semantic surface information. Furthermore, reconstructed surfaces from LIDAR data lack any inherent redundancy that can be utilized to enhance the accuracy of acquired data. In comparison to LIDAR systems, photogrammetry produces surfaces rich in semantic information that can be easily identified in the captured imagery. The redundancy associated with photogrammetric intersection results in highly accurate surfaces. However, the extended amount of time needed by the photogrammetric procedure to manually identify conjugate points in overlapping images is a major disadvantage. The automation of the matching problem is still an unreliable task especially when dealing with large scale imagery over urban areas. Also, photogrammetric surface reconstruction demands adequate control in the form of control points and/or GPS/INS units. In view of the complementary characteristics of LIDAR and photogrammetric systems, a more complete surface description can be achieved through the integration of both datasets. The advantages of both systems can be fully utilized only after successful registration of the photogrammetric and LIDAR data relative to a common reference frame. The adopted registration methodology has to define a set of basic components, mainly: registration primitives, mathematical function, and similarity assessment. This paper presents the description and implementation of a registration approach that utilizes straightline features derived from both datasets as the registration primitives. LIDAR lines are used as control for the imagery and are directly incorporated in the photogrammetric triangulation. The performance analysis is based on the quality of fit between the LIDAR and photogrammetric models including derived orthophotos.

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

Linear features for semi-automatic registration and change detection of multi-source imagery

Minimizing geometric and radiometric differences within images are key issues for change detection. To compensate for geometric differences, an accurate image registration method is suggested using the Modified Iterated Hough Transform (MIHT) as a matching strategy. Once geometric differences have been compensated, radiometric differences will be circumvented by using extracted edges, which are invariant to changes in the illumination conditions. Iterated Edge Filling (IEF) method is applied as a new change detection method. Several templates based on geometric shapes of artificial features are used to fill gaps between edges in each image. After minimizing geometric and radiometric differences, difference images are generated to analyze changed area. Experimental results using real data proved the feasibility of the suggested approach for deriving a quantitative estimate of changes among the registered temporal images. Keywordschange detection; registration; matching; linear feature; edge filling

Comparative analysis of the performance of metric-analog cameras, amateur-digital cameras, and LIDAR

The adaptation of digital technologies in today’s world is witnessing vast expansion where mapping tools are no exception. The increasing demand for rapid updates of spatial databases and the need for faster mapping end products are pushing towards using fast and cost-effective technologies. In traditional photogrammetric mapping, accurately calibrated analog metric cameras have been used to capture overlapping film photographs. Metric cameras are designed to provide extremely-high geometric image quality. They employ a low distortion lens system held in a fixed position relative to the film plane. The disadvantage of such systems is the high initial procurement cost and extra processing of photographs before measurements. The advent of low-cost off-the-shelf digital cameras encouraged both researchers and mapping companies to exploit such cameras in the mapping cycle. These cameras should be accurately calibrated and their accuracy and range of application should be determined. Besides photogrammetry, the evolving LIDAR (LIght Detection and Ranging) technology provides a new alternative for fast digital mapping. Based on a laser scanner and GPS/INS systems, a LIDAR system produces accurate point cloud measurements of surfaces and sometimes additional intensity images. Typical applications of LIDAR span mapping forestry floors, determination of power line sags, monitoring of coastal zones, city modeling, and construction surveys. The aim of this paper is to investigate the fit between the three mapping alternatives; metric-analog camera, low-cost/non-metric digital camera, and LIDAR. Two different types of cameras were used; Wild RC10 photogrammetric camera and Kodak 14n. Each camera was calibrated using a different calibration methodology and the number and arrangement of images taken were also different. As for the LIDAR dataset, an OPTECH ALTM 2050 laser scanner was used. Data from the laser range and reflected intensity were recorded. The comparative performance analysis is based on the quality of fit of the final alignment between the LIDAR and photogrammetric models through check-point analysis and derived orthophotos.