Jaebin Lee

Adjustment of Discrepancies Between LIDAR Data Strips Using Linear Features

Despite the recent developments in light detection and ranging systems, discrepancies between strips on overlapping areas persist due to the systematic errors. This letter presents an algorithm that can be used to detect and adjust such discrepancies. To achieve this, extracting conjugate features from the strips is a prerequisite step. In this letter, linear features are chosen as conjugate features because they can be accurately extracted from man-made structures in urban area and more easily extracted than the point features. Based on such a selection strategy, a simple and robust algorithm is proposed that is generally applicable for extracting such features. The algorithm includes methods that can be used to establish observation equations from similarity measurements of the extracted features. Then, several transformations are selected and used to adjust the strips. Following the transformation, the fitness of linear features is tested to determine whether the discrepancies have been resolved; the results are then evaluated statistically. The results demonstrate that the algorithm is effective in reducing the discrepancies between the strips.

An Automatic Registration Method for Adjustment of Relative Elevation Discrepancies between Lidar Data Strips

Despite the recent development in light detection and ranging (LIDAR) systems, discrepancies between strips in overlapping areas persist because of systematic errors. During the past decade, several methods have been developed for compensating for errors, such as checking the coincidence of conjugate features extracted from overlapping LIDAR strips or comparing interpolated range and intensity images. However, these approaches rely upon the ability to detect and extract suitable conjugate features within the overlap area and/or during the preprocessing of raw LIDAR data in, for example, interpolation or segmentation. Such procedures make the overall process complex and may impose limitations on the development of an automated method. Furthermore, some of the preprocessing techniques, such as raster interpolation, may induce errors in raw LIDAR data when dealing with large-scale coverage over an urban area. This paper therefore presents an automated approach, working with raw LIDAR data without the restrictions associated with using conjugate features and without any preprocessing. We present an approach using changes in local height variations that occur within the overlap area between two neighboring strips. In this case, local height variations of the LIDAR data in the overlap area increase if there are discrepancies. This scheme can be helpful in determining an appropriate transformation for the adjustment of discrepancies between neighboring LIDAR strips in a way that minimizes the local height variation. A contour tree (CT) was used to represent the local height variations and to find an appropriate initial transformaunction. The iterative closest point (ICP) procedure was then applied to refine the function parameters. Following transformation, LIDAR strips were registered with each other, and the discrepancies were measured again to determine whether they had been resolved. The statistical evaluation of the results revealed that the discrepancies were significantly reduced.

Segmentation and extraction of linear features for detecting discrepancies between LIDAR data strips

Discrepancies in LIDAR data strips still exist despite the development of the system and many related researches because of its own systematic characteristics. This paper presents an algorithm for detection and adjustment of discrepancies between LIDAR strips. The extraction of conjugate features in overlapping LIDAR strips is a prerequisite step. In this paper, a simple and robust algorithm is proposed to automatically extract the linear features, which are used as conjugate features. With these linear features, discrepancies are measured and adjusted by applying 3D similarity transformation.

Adjustment for discrepancies between ALS data strips using a contour tree algorithm

In adjusting for discrepancies between adjacent airborne laser scanning (ALS) data strips, previous studies generally used conjugate features such as points, lines, and surface objects; however, irrespective of the types of features employed, the adjustment process relies upon the existence of suitable conjugate features within the overlapping area and the ability of the employed method to detect and extract the features. These limitations make the process complex and sometimes limit the applicability of developed methodologies because of a lack of suitable features in overlapping areas. To address these problems, this paper presents a methodology that uses the topological characteristics of the terrain itself, which is represented by a contour tree (CT). This approach provides a robust methodology without the restrictions involved in methods that employ conjugate features. Our method also makes the overall process of adjustment generally applicable and automated.

RPC model generation from the physical sensor model

The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective, the linear pushbroom and the SAR. This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1 (Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects 510 ~ 710 nm panchromatic images with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.