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Proceedings Paper

Hyperspectral clustering and unmixing for studying the ecology of state formation and complex societies
Author(s): Justin D. Kwong; David W. Messinger; William D. Middleton
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Paper Abstract

This project is an application of hyperspectral classification and unmixing in support of an ongoing archaeological study. The study region is the Oaxaca Valley located in the state of Oaxaca, Mexico on the southern coast. This was the birthplace of the Zapotec civilization which grew into a complex state level society. Hyperion imagery is being collected over a 30,000 km2 area. Classification maps of regions of interest are generated using K-means clustering and a novel algorithm called Gradient Flow. Gradient Flow departs from conventional stochastic or deterministic approaches, using graph theory to cluster spectral data. Spectral unmixing is conducted using the RIT developed algorithm Max-D to automatically find end members. Stepwise unmixing is performed to better model the data using the end members found be Max-D. Data are efficiently shared between imaging scientists and archaeologists using Google Earth to stream images over the internet rather than downloading them. The overall goal of the project is to provide archaeologists with useful information maps without having to interpret the raw data.

Paper Details

Date Published: 17 August 2009
PDF: 9 pages
Proc. SPIE 7457, Imaging Spectrometry XIV, 74570E (17 August 2009); doi: 10.1117/12.826354
Show Author Affiliations
Justin D. Kwong, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
William D. Middleton, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 7457:
Imaging Spectrometry XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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