Unsupervised Classification System for Hyperspectral Data Analysis
Abstract
There is an increasing interest in remote sensed vision systems for surveillance, object recognition, target identification, and cartography. Other fields that could benefit from such systems are land use and land cover administration, estimation of water sedimentation, and the creation of maps. Of particular interest are automatic systems that are robust with respect to analyst's knowledge in areas such as image analysis and computer vision. Remote Sensed image analysis focuses on obtaining information from radar, multi spectral and hyperspectral images. Hyperspectral data enables the analyst to detect more materials, objects. and regions with more accuracy than previously possible. As the number of bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase and the classification accuracy should increase as well. The curse of dimensionality has been known for more than three decades. There is a need for the development of algorithms for detection, and classification that utilize the amount of information and separability that hyperdimensional data offers while simultaneously avoiding the difficulties inherent in hyperdimensional space. The present report will summarize the research done in the areas of clustering, parameter estimation of hyperspectral data, band subset selection, data compression, and unsupervised decision fusion mechanism.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2001
- Accession Number
- ADA398803
Entities
People
- Luis O. Jimenez
- Miguel Velez
- Shawn Hunt
Organizations
- University of Puerto Rico at Mayaguez