[GD11-3] Algorithms and processing

Introduction

Learning outcomes

• 765 - Differentiate supervised classification from unsupervised classification
  Differentiate supervised classification from unsupervised classification
• 766 - Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering
  Produce pseudocode for common unsupervised classification algorithms including chain method, ISODATA method, and clustering
• 767 - Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
  Perform a manual unsupervised classification given a two-dimensional array of reflectance values and ranges of reflectance values associated with a given number of land cover categories
• 768 - Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
  Calculate a set of filtered reflectance values for a given array of reflectance values and a digital image filtering algorithm
• 769 - Describe a situation in which filtered data are more useful than the original unfiltered data
  Describe a situation in which filtered data are more useful than the original unfiltered data
• 770 - Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
  Describe the sequence of tasks involved in the geometric correction of the Advanced Very High Resolution Radiometer (AVHRR) Global Land Dataset
• 771 - Compare pixel-based image classification methods with segmentation techniques
  Compare pixel-based image classification methods with segmentation techniques
• 772 - Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
  Explain how to enhance contrast of reflectance values clustered within a narrow band of wavelengths
• 773 - Describe an application of hyperspectral image data
  Describe an application of hyperspectral image data

Self assessment