For this topic we will focus on raster data, which is the common data type for imagery analysis in remote sensing. Understanding basic image enhancement and spatial filters is an important step in analyzing raster data. Classification is a common technique used to efficiently categorize land-cover, however being able to explain error and understand accuracy assessments is vital to justifying your classification.
Choosing which spectral channels or bands to be represented on your colour guns provides a variety of strategies that can allow you to meaningfully interpret your image. The enhancements and filters we have gone over is not an exhaustive list, there are countless other ways to filter images, but we have introduced the basic ones to get you started. Classification is a very important tool that can be applied to a variety of different fields, how might you apply classification to improve sustainable forest management? Lastly, accuracy assessments need to be conducted to validate classifications for yourself, but also for your colleagues or potential employers.
Topic Self-review (For self learning)
Please use the reflection questions below as study guide to conduct self-review for the topic.
List and describe several different kinds of image enhancements and filters.
Explain why we would want to classify images.
Describe the procedures, pros and cons associated with supervised and unsupervised classification.
What causes classification error?
How do you conduct an accuracy assessment?
FODE009
Requirements Changed
Module IV Introduction Topic 4.2: Temporal Image Analysis