Ron Li

3-D Shoreline Extraction from IKONOS Satellite Imagery

Shorelines are recognized as unique features on Earth. They have valuable properties for a diverse user community. At present, photogrammetry is the most popular technique used to capture a shoreline. With improved resolution and accuracy, commercial high-resolution satellite imagery is demonstrating a great potential in the photogrammetry application domain. One example is the utilization of IKONOS satellite imagery in shoreline extraction. IKONOS panchromatic imagery has a resolution of approximately one meter as well as the capabilities of stereo imaging. This article presents the results of an experiment in which we attempted to improve IKONOS Rational Functions (RF) for a better ground accuracy and to employ the improved RF for 3-D shoreline extraction using 1-meter panchromatic stereo images in a Lake Erie coastal area. Two approaches were investigated. One was to rectify the ground coordinates derived from vendor-provided RF coefficients using ground control points (GCPs). The other was to refine the RF coefficients using the GCPs. We compare the results from these two approaches. An assessment of the shoreline extracted from IKONOS images compared with the existing shoreline is also conducted to demonstrate the potential of the IKONOS imagery for shoreline mapping.

Digitalization of coastal management and decision making supported by multi-dimensional geospatial information and analysis

This paper summarizes the results and outcomes of our NSF Digital Government project. During four years, this project concentrated on investigating, integrating and developing geospatial technologies to enhance the operational capabilities of federal, state, and local agencies responsible for coastal management and decision making. Successful collaborations have been established among research laboratories and government agencies in the areas of data collection and analysis, hydrodynamic modeling, development of web-based systems, and real-world applications of research results.

A Vision for Cyberinfrastructure for Coastal Forecasting and Change Analysis

This paper gives an overview of a recently initiated cyberinfrastructure project at The Ohio State University. This project proposes to develop and evaluate a cyberinfrastructure component for environmental applications. This will include developments in middleware, model integration, analysis, and mining techniques, and the use of a service model for supporting two closely related applications. These applications will be real-time coastal nowcasting and forecasting, and long-term coastal erosion analysis and prediction.

Object modeling and matching from multi-view ground images for automated Mars rover localization

This paper presents an innovative method for object modeling and matching from multi-view ground images for automated Mars rover localization. In this method, rocks are first extracted from a selection of 3D ground points that are generated from dense matching. The extracted rocks are then modeled using analytical surfaces such as ellipsoids, hemispheres, cones, and tetrahedrons. The extracted rocks of two rover stations are matched through a robust algorithm that matches the geometric configuration patterns of the rocks from the two stations using an improved Hough transform technique, followed by a heuristic refinement. Finally, peaks of the matched rocks serve as cross-site tie points in bundle adjustment of the rover image network. Experiments conducted using Navcam images acquired from the 2003 Mars Exploration Rover mission have demonstrated that the proposed method is capable of selecting cross-site tie points for two rover stations that are 26 m apart. The issues of integration of visual odometry with bundle-adjustment are also briefly discussed