Soo Jeong

Tracking Road Centerlines from High Resolution Remote Sensing Images by Least Squares Correlation Matching

This paper describes a semi-automatic algorithm for tracking road centerlines from satellite images at 1 m resolution. We assume that road centerlines are visible in the image and that among points on road centerlines similarity transformation holds. Previous approaches proposed for semi-automatic road extraction include energy minimization and template matching with global enforcement. In this paper we will show that least squares correlation matching alone can work for tracking road centerlines. Our algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. An Ikonos image over Seoul area was used for test. The algorithm could successfully extract road centerlines once valid input points were provided from a user. The contribution of this paper is that we proved template matching could offer wider applicability in feature extraction, and we designed a new template matching scheme that worked for feature extraction without global enforcements.

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.

Automatic DEM generation using IKONOS stereo imagery

This paper introduces two methods for DEM generation in IKONOS stereo imagery using modified rational polynomial coefficients (RPC). The Rational Function Model (RFM) is the sensor model of IKONOS imagery for end-users. IKONOS imagery vendors provide RPC, RFM coefficients for IKONOS, for end-users with imagery. So it is possible that end-users obtain geospatial information in their IKONOS imagery without any additional effort. But there are still requirements for the rigorous DEM on RPC user. In IKONOS imagery, physical sensor modeling is difficult because IKONOS vendors do not provide satellite ephemeris data and abstract sensor modeling requires many ground control points (GCPs) well distributed in the whole image as well as other satellite imagery. Therefore RPC modification is the better choice. If a small number of GCPs is available, DEM can be generated using modified RPC by methods which are introduced in this paper. Study on DEM generation using modified RPC in IKONOS guarantees result reliability. Pseudo GCP generated with vendors RPC and additional GCP make it possible through sequential solution.

Hierarchical corner matching for automatic relative orientation

This paper describes the automatic relative orientation technique with a corner matching. A hierarchical feature extraction is used to detect overlapped regions between two stereo images. The extracted features are matched and filtered through the vector filtering process. The matched features are used to compute relative orientation parameters. An automatic feature extraction process contributes to the automation of relative orientation.