Outline


  1. Motivation/Background: HSI classification
    1. What is HSI?
      1. Comparison to RGB
    2. The advantages of HSI over other kinds of data
      1. Instant, remote, nondestructive data collection
      2. Hundreds of data points per pixel
    3. What is classification?
      1. Introduce using toy data set, like CUB-200
    4. Applications of HSI classification
      1. Cancer detection
      2. Art authentication
      3. Food quality analysis
      4. Remote sensing
        1. Urban development
        2. agricultural monitoring
        3. environmental assessment
    5. Problems faced
      1. High dimensionality
      2. Scarcity of labeled data
  2. Method: MCAE and Ladder Network
    1. Feature Extractors
      1. Baseline: PCA
      2. Inspiration: Autoencoder
      3. Our proposed method: MCAE
    2. Classifiers
      1. Baseline: SVM
      2. Inspiration: Neural network
      3. Our proposed method: Ladder network
  3. Results: State-of-the-art Comparison
    1. Comparison of MCAE against PCA and state-of-the-art
    2. Comparison of Ladder Network against SVM and state-of-the-art


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