The timely analysis and exploitation of data from multispectral/hyperspectral sensors from remote sensing platforms can be a daunting task. One such sensor platform is the Multispectral Thermal Imager (MTI), which provides a highly informative source of remote sensing data. In a typical exploitation scenario, an image analyst may need to consistently locate regions/objects of interest from a stream of imagery in a timely manner. Many available image analysis/segmentation techniques are often either slow, not robust to spectral variabilities from view to view or within a spectrally similar region, or may require a significant amount of user intervention including a priori knowledge to achieve a segmentation corresponding to self-similar regions within the data. This paper discusses an unsupervised segmentation approach that exploits the gross spectral shape of MTI data. We describe a nonparametric unsupervised approach based on a graph theoretic representation of the data. The goal of this approach is to perform coarse level segmentation that can stand alone or as a potential precursor to other image analysis tools. In comparison to previous techniques, the key characteristics of this approach are in its simplicity, speed, and consistency. Most importantly it requires few user inputs and determines the number of spectral clusters, their overall size, and subsequent pixel assignment directly from the data.