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Principal Investigator Dr. Ron Li, Lowber B.
Strange Professor, Director, Mapping
& GIS Laboratory; The Ohio State University Co-Principal
Investigators Dr. Deliang Wang, Department of Computer Science and Engineering; The Ohio State University
Research Staff and Graduate Research Assistants Dr. Bo Wu, Research Associate, Mapping & GIS Laboratory; The Ohio State University Lin Yan, PhD Student, Mapping & GIS Laboratory; The Ohio State University Jiangye Yuan, Master Student, Department of Computer Science and Engineering; The Ohio State University
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Over the last decades, there has been a remarkable increase in the number of remote sensing sensors on-board various satellite, aircraft, and land vehicle platforms. Large volumes of panchromatic, multispectral, and hyperspectral data with high resolutions (e.g., meter to sub-meter level for satellites) have been collected periodically. Fusion of these multidimensional remote sensing data along with in situ observations from multiple sensors can help us to derive more information than is possible from a single sensor alone. Examples include the determination of the composition of ground vegetation, localization of mineral resources, and other traditional application areas. However, the more detailed information such as shape and object type cannot often be derived precisely. New automatic target recognition methods that can distinguish critical object attribute and geometric characteristics would allow extraction of scale and rotationally variant objects and targets in a scene, or distinguishing two objects made of the same material. Recent development and advances in the biologically inspired methods involve segmenting patterns, materials, and objects, among other capabilities. If such human perception supported object recognition methods can be combined with much developed geometry based object recognition techniques, a variety of object recognition tasks in civilian, military, and intelligent applications can be significantly improved and speed up. They can also open up new application fields that may be otherwise impossible. In this research, we will develop an innovative biologically and geometrically inspired approach to target recognition for multispectral/hyperspectral and multiplatform image analysis. The
objectives of this research project are to: Develop a biologically inspired and extended algorithm of S-LEGION (Spatial LEGION) to quickly analyze and extract information from multispectral/hyperspectral remote sensing images covering large areas, Develop a new vector geometric active contour (GAC) model for target boundary extraction, refinement and shape reconstruction in interest areas extracted by S-LEGION, Research on a memory-based target recognition method based on S-LEGION that can perform the final recognition of interested targets considering spectral, contextual, and geometric patterns, and Establish an integrated multi-sensor (satellite, airborne, descent, and ground) model for scale and rotationally variant object extraction and target recognition across multiple platforms to support the biologically and geometrically inspired approach. |
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Publications |
September
2007 - August 2009 ·
Wu,
B., Y. Zhou, L. Yan, J. Yuan, R. Li, and D. Wang 2009. Object
Detection from Hyperspectral/Multispectral and Multi-Platform Remote-Sensing
Imagery by the Integration of Biologically and Geometrically Inspired
Approaches. Proceedings of the ASPRS 2009 Annual Conference,
Baltimore, MD, March 9-13, 2009, 7 p. ·
Yuan,
J., D.L. Wang, B. Wu, L. Yan, and R. Li 2009. Automatic Road Extraction from
Satellite Imagery Using LEGION Networks. International Joint Conference on
Neural Networks, Atlanta, GA, June 14-19, 2008, 8p. ·
Zhou,
Y., B. Wu, D. Li, and R. Li 2009. Edge Detection on Hyperspectral Imagery via
Manifold Techniques. Proceedings of the First IEEE Workshop on Hyperspectral
Image and Signal Processing, Grenoble, France, August 26 - 28, 2009, 4p. |
Last updated: April, 2009
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