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.
The Multispectral Thermal Imager (MTI) provides a highly informative source of remote sensing data. However, the analysis and exploitation can be very challenging. Effective utilization of this imagery by an image analyst typically requires a consistent and timely means of locating regions of interest. Many available image analysis/segmentation techniques are often slow, not robust to spectral variabilities from view to view or within a spectrally similar region, and/or require a significant amount of user intervention to achieve a segmentation corresponding to self-similar regions within the data. This paper discusses a segmentation approach that exploits the gross spectral shape of MTI data. In particular, we propose a nonparametric approach 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 relatively few user inputs and determines the number of clusters, their extent, and, data assignment directly from the data.
Automating the detection and identification of significant threats using multispectral (MS) imagery is a critical issue in remote sensing. Unlike previous multispectral target recognition approaches, we utilize a three-stage process that not only takes into account the spectral content, but also the spatial information. The first stage applies a matched filter to the calibrated MS data. Here, the matched filter is tuned to the spectral components of a given target and produces an image intensity map of where the best matches occur. The second stage represents a novel detection algorithm, known as the focus of attention (FOA) stage. The FOA performs an initial screening of the data based on intensity and size checks on the matched filter output. Next, using the target's pure components, the third stage performs constrained linear unmixing on MS pixels within the FOA detected regions. Knowledge sources derived from this process are combined using a sequential probability ratio test (SPRT). The SPRT can fuse contaminated, uncertain and disparate information from multiple sources. We demonstrate our approach on identifying a specific target using actual data collected in ideal conditions and also use approximately 35 square kilometers of urban clutter as false alarm data.
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