Gi-Hong Kim

Road Infrastructure Data Acquisition Using a Vehicle‐Based Mobile Mapping System

This study presents the technology of a vehicle-based mobile mapping system to maintain an up- dated transportation database. The mobile mapping sys- tem that integrates the global positioning system (GPS), the inertial navigation system (INS), and digital cameras has been developed to collect data on position and at- tributes of road infrastructure. The vehicle-based mobile mapping system works by having the GPS and INS record the position and attitude data, and digital cameras take road images. The stereovision system can determine the position of objects that are visible on the image pair in the global coordinate system with GPS and INS data. As field data acquisition is a very expensive task, a mo- bile mapping system offers a greatly improved solution. In this study, we successfully created a road infrastruc- ture map with mobile mapping technology and proposed automatic algorithms for detecting and identifying road signs from road images. The proposed detection algo- rithm includes line and color region extraction processes and uses the Hopfield neural networks. The identification algorithm uses seven invariant moments and parameters that present geometric characteristics. With this combined method, we could successfully detect and identify road signs.

Radargrammetry for DEM generation using minimal control points

The erroneous satellite orbit causes significant distortions to the result of DEM (Digital Elevation Model) generated by radargrammetry. If accurate ephemeris data of satellite are available, satellite orbit parameters can be exactly determined without CPs (control points). If not, it must be refined by using the well defined CPs. For the refinement of orbit parameters, the acquisition of sufficient CPs is the most tedious and time consuming step and obstacle to near real time applications, especially for SAR (Synthetic Aperture Radar) imagery. This paper describes orbit parameters refinement method for radargrammetry using ephemeris data and only a single control point to resolve the CPs problem. The proposed method is applied to RADARSAT-1 SGF images of which ephemeris information is not accurate to directly use for accurate geometric modeling. The RMSE of the DEM generated by using a single CP (Control Point) is 43.86m, while that of the DEM generated by traditional method using 10 CPs is 45.79m in comparison with reference DEM that is generated from 1:5,000 digital topographic maps.

Road change detection algorithms in remote sensing environment

This paper describes an automatic change detection of roads using aerial photos and digital maps. The task is based on the idea that one can derive information about the changes strictly from its imagery once the geometric relationship among data sets is correctly recovered. The goal of research is achieved by using the Modified Iterated Hough Transform (MIHT) algorithm, the result of which not only solves the orientation parameters of the aerial camera but also filters out blunders from all possible combination of their entities. To examine the effectiveness of the MIHT algorithm, a digital road map and an aerial photo are used to detect changes. Experimental results demonstrate the potential of the MIHT algorithm for detecting changes of the Geospatial Information System (GIS) data.

Rigorous sensor modeling of early reconnaissance CORONA imagery for monitoring urban growth

We describe geometric modeling processes to remove the severe panoramic distortion of old era CORONA KH4B imagery. We implement the modified collinear equation and rational function model. We can obtain less than 2 pixel horizontal and vertical accuracy. We successfully create a digital elevation model of a test site and orthoimages. With the result of sensor modeling an urban expansion study is accomplished using more recent SPOT imagery.

Correction of building height effect using LIDAR and GPS

Correction of building height effects is a critical step in image interpretation from aerial imagery in urban area. In this paper, an efficient scheme to correct building height effects from aerial color imagery using multi source data sets is presented. The following steps have been performed to remove the shadow effect from aerial color imagery. First, the shadow regions of the orthorectified aerial color image are precisely located using the solar position and the heights of ground objects derived from LIDAR (Light Detection and Ranging) data. The shadow area is composed of many different ground features. To accurately recover the original spectral information the step for accurate segmentation of shadow regions needed. This step has been performed by utilizing the existing digital map which contains surface cover information. To assign correction information corresponding to the surface features on the shadowed area, correction factor needed to be modeled. For this three basic assumptions are proposed and comparison between the context region and the same non-shadowed context region is made. Finally, the shadow-effect-corrected image is generated by using the correction factor obtained from the previous step.