Mr. Justin D. Kwong Profile

Justin D. Kwong

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Proceedings Article | 18 August 2009 Paper

Hyperspectral clustering and unmixing for studying the ecology of state formation and complex societies

Justin Kwong, David Messinger, William Middleton

Proceedings Volume 7457, 74570E (2009) https://doi.org/10.1117/12.826354

KEYWORDS: Algorithm development, Spatial resolution, Optical inspection, Ecology, Remote sensing, Vegetation, Data modeling, Satellites, Sensors, Earth observing sensors

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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.

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