<div class="description">
<p>In this thesis, a three-stage algorithm for performing unsupervised segmentation of hyperspectral imagery is developed and tested. Based on a data-model derived from common sources of error inherent in all imaging spectrometers, each stage of the algorithm leverages modified clustering methods that incorporate both the spatial and spectral information present within the hyperspectral scene. The ultimate output of the process is a classification map that groups spatially adjoining pixels into a number of distinct regions, each of which can be well approximated by a multivariate normal distribution. This output is expected to be useful in various target detection scenarios that require accurate estimates of background statistics; an anecdotal example of improved subpixel target detection is provided at the end of the thesis. Execution time and memory metrics are provided for each stage of the algorithm, along with segmentation proficiency scores as measured against random synthetic data sets. Segmentation results of real-world hyperspectral images are also provided for qualitative analysis. We conclude from the experiments that the algorithm usually performs well in typical overhead views with multiple, spectrally diverse objects to segment, but suffers from reduced accuracy in scenarios where individual regions are exceptionally large or small. Future improvements to the algorithm are recommended to potentially overcome these drawbacks.
</p>
<br>Subjects: hyperspectral; image; segmentation; clustering; k-means; hierarchical agglomerative clustering; community detection; SLIC; Ward’s method; energy distance; modularity; spectral; spatial</div>
<div class="fn value">
Kennedy, Sean M.</div>
Internet Archive identifier