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Exploiting Hyperspectral Hypertemporal Imagery with Feature Clustering for Invasive Species Detection

This paper presents a feature extraction method for exploiting hyperspectral hypertemporal data and applies the new method to the problem of invasive species detection. By definition, hyperspectral hypertemporal imagery is very high dimensional data, and dimensionality reduction will play a critical role in utilizing such data. We present a feature extraction method that takes advantage of the high correlation among elements of the spectral and temporal feature space. This high correlation can be attributed to the premise that the changes in the reflectance of closely spaced wavelengths do not always change dramatically over short periods of time. The proposed feature clustering method is based on the assumption that adjacent elements in the spectro-temporal feature space are highly correlated and can be grouped together to form lower- dimensional feature spaces. The proposed hyperspectral hypertemporal feature clustering method is tested and validated within an invasive vegetation detection application. The hypothesis is that as time progresses, the spectral response of different plant species change differently. Thus, there should be hyperspectral hypertemporal features that can be used to discriminate between the vegetative species. Additionally, the results of the feature clustering method can be used to determine which regions of the spectrum and which collection dates are optimum for the given invasives detection problem.