2017 Student Research

     Title: Analysis of Spectral Reflectance and Separability of Vegetation for fireMAP
     Thesis Adviser: Dr. Dale Hamilton
My senior project was Data Collection, Analysis, and Storage for the Fire Monitoring and Assessment Platform (FireMAP) project. This involved gathering samples of live vegetation and ash from burn sites and running spectroscopy tests on the samples. I was focusing on the Ultraviolet to Near IR spectral range (190 nm- 900 nm) to determine which spectra showed the most noticeable separation in terms of the samples’ reflectance values. After running statistical t-tests on the data, we found separation in the visible light spectra. This allows us to conclude that machine learning classifiers should be able to differentiate between each of our classes of interest (canopy fuel, surface fuel, black ash, and white ash) using normal color imagery instead of requiring any hyperspectral sensors.



    Title: Fire Monitoring and Assessment Platform: Image Post-processing and Image Manipulation
    Thesis Adviser: Dr. Barry Myers, Dr. Dale Hamilton
FireMAP is a NNU research project which uses machine learning to map fire severity imagery. When this post-classification image processing component receives the imagery, the pixels have already been spectrally classified depicting classes of interest such as unburned vegetation, black ash and white ash. Noise and rough edges are removed from the imagery resulting in a clearer and less cluttered representation of the fire severity. High severity areas, identified by white ash, are much smaller in the imagery than in the actual high severity burn areas, so they are morphologically dilated to better represent the actual high severity areas. Lastly, due to unnecessarily high image resolution, the imagery contains excess data, so image resolution is reduced to negate excess data and decrease image storage size.


    Title: A kNN Classification using KD Trees
    Thesis Adviser: Dr. Dale Hamilton
My project is called "Object Based Classification" and the purpose for the project is to process varying forms of imagery and extract useful information from the images. The current two implementations of the program are classifying fire extent and severity for NNU's FireMAP project and to identify possible prostate cancer in prostate smears in collaboration with Dr. Joe Kronz. Currently the project is at a point where it can quickly and accurately identify individual pixels within images but it cannot identify groups of pixels (objects). The expected result of the program is for the program to be capable of accurately identify both individual pixels and objects.


    Title: Diagnosing and Rebuilding a Server System After a Major Failure
    Thesis Adviser: Dr. Dale Hamilton
My projects goal was to get the departmental server system back online and add improvements to the system. Some of the improvements are increased storage, updated software, and task management. I expect the server system to be working better than ever and be well documented so future system workers will be able to understand how the system is set up. 


    Title: Object Identification in High Resolution Images
    Thesis Adviser: Dr. Dale Hamilton
 My project through FireMAP is called the "Object Identifier." The goal of this project is to be able to take an image and group like pixels in it together to form objects. With objects it is easier to extrapolate data and make observations in higher resolution imagery. This project is expected to fairly accurately group pixels together that are similar spectrally (look alike) and spatially (near each other). These objects will represent actual objects in the imagery to ease the image classification process. This project breaks grounds in that imagery of this resolution is data based on objects comprised of pixels instead of pixels comprised of objects, and through machine learning details of these objects such as: size, shape, and texture, can be utilized for classifying them.

    Title: Four-Band Image Acquisition System
    Thesis Adviser: Dr. Dale Hamilton
Valuable information about a plant’s health, moisture content, and even species identification can be found by analyzing the plant reflects light in the blue, green, red, and near-infrared bands (often referred to as RGB and NIR). The FireMAP project can use this information to draw conclusions about wildland conditions and how they relate to fire behavior and severity. Commercially available sensors that collect this four-band data are prohibitively expensive, and converting consumer-grade cameras to capture near-infrared data requires sacrificing data from one of the other bands. For my Senior Project, I’m designing, assembling, and programming a camera that will collect data in all four reflectance bands, as well as related location data.

Almost 100% of NNU’s computer science majors graduate in four years, not only saving the cost of a 5th year of college required by most computer science majors at other universities—but also earning a lucrative beginning salary.