Research Presentation Outline

This is the outline for the presentation I will be giving on my research project:
Presentation Outline

Reason/Explanation:
  • Explain Project: My project is to research different parts/steps of a machine learning pipeline.
  • Explain Pipeline: The pipeline is a framework that uses previously classified hyperspectral imaging (HSI) data to train itself to classify other HSI data.
    •  Explain HSI: Hyperspectral imaging is collecting image data on objects (and in this case landscapes) that is not limited to visible light, detecting a broader range of the electromagnetic spectrum including ultraviolet and infrared light.
    • Explain Labels/Classification: Once HSI data is collected, different portions of the data must be classified. In a hyperspectral image of a landscape, each pixel must be labeled as a part of what it makes up. For example, if a pixel is part of a car or a tree in the image, it must have the car or tree label. Labels are known as classes.
  • Give Reason for Project: It is inefficient to classify every pixel in a hyperspectral image manually. A program must be trained to label each element in an image or group of images as well as a human can. This program, the pipeline which I am testing, should be able to train itself on a small portion of labeled data and be able to accurately label the remainder of the data based on the training. I am evaluating different parts of the pipeline such as preprocessors, feature extractors, and classifiers to determine the accuracy of different types of each. To test these, I am using two sets of already classified HSI data, the Indian Pines dataset and the Pavia University dataset.
  • Explain Parts of the Pipeline:
    • Preprocessors
    • Feature Extractors
    • Classifiers
    • Others (Not sure yet)
Methods and Results of Each Experiment:
  • First Experiment: Evaluate Preprocessors
    • Method:
      • Part One: All Seven
      • Part Two: PCA with and without Whitening
    • Results:
      • Part One Results: See Table 1
      • Part Two Results: Coming Soon
  • Second Experiment: Evaluate Classifiers
    • Method
    • Results
  • Other Experiments
Conclusion


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