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Proceedings Paper

Cotton crop spectral imaging analysis: a web-based hyperspectral synthetic imagery simulation system
Author(s): Vladimir J. Alarcon; Gretchen F. Sassenrath
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Paper Abstract

The development of spectral libraries for specific vegetation species and soils is useful for identifying different physiological or physical-chemical characteristics. Usually, spectral libraries are provided as a data-base add-in of current commercial software used for analyzing hyperspectral imagery. The use of those databases requires installation of the software in the user’s machine for either visualizing or using the spectral libraries. There are also spectral libraries available on the web but the data is static and partitioned by spectrum of vegetation or soil because the size of the files of actual hyperspectral images precludes it’s publication on the web. In this paper, a web-based simulation environment for generating hyperspectral synthetic imagery of cotton plots is presented. The system was developed using Java and is based on a previous synthetic imagery program1. The mathematical and numerical formulation of the model is briefly sketched. The core computing components of the simulation environment were written in C for their computational efficiency. The emerging Java Native Interface (JNI) technique and standard Java techniques were used to design a user-friendly simulator. The simulation system provides interactive user control and real time visualization of the resulting hyperspectral image through standard web browsers. It shows potential for providing web-based hyperspectral libraries, in the form of images, for public use.

Paper Details

Date Published: 9 November 2004
PDF: 8 pages
Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); doi: 10.1117/12.559671
Show Author Affiliations
Vladimir J. Alarcon, Mississippi State Univ. (United States)
Gretchen F. Sassenrath, USDA Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 5544:
Remote Sensing and Modeling of Ecosystems for Sustainability
Wei Gao; David R. Shaw, Editor(s)

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