Joshua B. Broadwater

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Subpixel detection is a challenging problem in hyperspectral imagery analysis. Since the target size is smaller than the size of a pixel, detection algorithms must rely solely on spectral information. A number of different algorithms have been developed over the years to accomplish this task, but most detectors have taken either a purely statistical or a(More)
A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each endmember within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become(More)
In previous work, kernel methods were introduced as a way to generalize the linear mixing model. This work led to a new set of algorithms that performed the unmixing of hyperspectral imagery in a reproducing kernel Hilbert space. By processing the imagery in this space different types of unmixing could be introduced - including an approximation of intimate(More)
A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based(More)
In previous work, kernel methods were introduced as a way to generalize the linear mixing model for hyperspectral data. This work led to a new physics-based kernel that allowed accurate unmixing of intimate mixtures. Unfortunately, the new physics-based kernel did not perform well on linear mixtures; thus, different kernels had to be used for different(More)
In previous work, kernel methods were introduced as a way to generalize the linear mixing model for hyperspectral data. This work led to a new adaptive kernel unmixing method that both identified and unmixed linearly and intimately mixed pixels. However, the results from this previous research was limited to lab-based data where the endmembers were known(More)
Determining a detection threshold to automatically maintain a low false alarm rate is a challenging problem. In a number of different applications, the underlying parametric assumptions of most automatic target detection algorithms are invalid. Therefore, thresholds derived using these incorrect distribution assumptions do not produce desirable results when(More)
Numerous subpixel detection algorithms utilizing structured backgrounds have been developed over the past few years. These range from detection schemes based on spectral unmixing to generalized likelihood ratio tests. Spectral unmixing algorithms such as the Fully Constrained Least Squares (FCLS) algorithm have the advantage of physically modeling the(More)
Recent advances in electronics and sensor design have enabled the development of a hyperspectral video camera, which can capture hyperspectral datacubes at near video rates. In this work, we show how high-speed hyperspectral imaging can be used to address several challenging problems in video surveillance. In particular, we combine traditional methods for(More)
A significant amount of research has gone towards identifying the correct number of endmembers for a scene. Most algorithms have focused on what has been termed “intrinsic” dimensionality [1]. These dimensionality measures such as the Akaike Information Criterion (AIC) [2], the Minimum Description Length (MDL) [3], and the Empirical Indicator Function (EIF)(More)