Podcast
Questions and Answers
What is the purpose of plotting pixel observations on a scatter diagram?
What is the purpose of plotting pixel observations on a scatter diagram?
- To convert digital values to analog signals
- To calculate the distance between pixels
- To visualize each pixel's spectral properties (correct)
- To determine the exact cover type of each pixel
Which classifier uses the concept of 'Minimum-distance-to-means'?
Which classifier uses the concept of 'Minimum-distance-to-means'?
- Parallelepiped Classifier (correct)
- Spectral Angle Mapper
- K-nearest neighbors Classifier
- Gaussian Maximum Likelihood Classifier
In the scatter diagram, what does a point at coordinate (10, 68) represent?
In the scatter diagram, what does a point at coordinate (10, 68) represent?
- Band 4 DN for a pixel (correct)
- Band 3 DN for a pixel
- An unidentified cover type
- A training set description
What do the 'clouds of points' in the scatter diagram represent?
What do the 'clouds of points' in the scatter diagram represent?
Why are pixel observations from areas of known cover type used as 'training sets'?
Why are pixel observations from areas of known cover type used as 'training sets'?
What is the main advantage of using multidimensional descriptions of spectral response patterns in classification strategies?
What is the main advantage of using multidimensional descriptions of spectral response patterns in classification strategies?
What is the first stage in a typical supervised classification procedure?
What is the first stage in a typical supervised classification procedure?
How are pixels classified in the classification stage of a supervised classification procedure?
How are pixels classified in the classification stage of a supervised classification procedure?
What label is assigned to a pixel that is insufficiently similar to any training dataset during classification?
What label is assigned to a pixel that is insufficiently similar to any training dataset during classification?
What type of products are typically generated as outputs of the supervised classification process?
What type of products are typically generated as outputs of the supervised classification process?
What happens if a pixel is categorized into a land cover class it most closely resembles during classification?
What happens if a pixel is categorized into a land cover class it most closely resembles during classification?
How do spectral response patterns play a role in image classification?
How do spectral response patterns play a role in image classification?
What is one advantage of using supervised training data in ISODATA?
What is one advantage of using supervised training data in ISODATA?
Why might a multistage approach be used with unsupervised classification?
Why might a multistage approach be used with unsupervised classification?
What is the purpose of masking the 'problem' class in the second stage of classification?
What is the purpose of masking the 'problem' class in the second stage of classification?
How does the second-stage classification step contribute to the overall process?
How does the second-stage classification step contribute to the overall process?
What happens during the recoding output from the second-stage classification?
What happens during the recoding output from the second-stage classification?
Why is the merger of classification results important in this approach?
Why is the merger of classification results important in this approach?
What is the primary function of unsupervised classifiers in pixel classification?
What is the primary function of unsupervised classifiers in pixel classification?
How does the Bayesian classifier differ from maximum likelihood classification?
How does the Bayesian classifier differ from maximum likelihood classification?
What technique is employed to reduce computational complexity in maximum likelihood classification?
What technique is employed to reduce computational complexity in maximum likelihood classification?
Which clustering algorithm requires the analyst to specify the number of clusters?
Which clustering algorithm requires the analyst to specify the number of clusters?
What is the main challenge of maximum likelihood classification for datasets with many spectral channels or classes?
What is the main challenge of maximum likelihood classification for datasets with many spectral channels or classes?
What do unsupervised classifiers initially classify pixels as?
What do unsupervised classifiers initially classify pixels as?
What is the main objective of hybrid supervised/unsupervised classification methods?
What is the main objective of hybrid supervised/unsupervised classification methods?
In hybrid supervised/unsupervised classification, why are unsupervised training areas intentionally selected?
In hybrid supervised/unsupervised classification, why are unsupervised training areas intentionally selected?
What is the role of guided clustering in hybrid supervised/unsupervised classification?
What is the role of guided clustering in hybrid supervised/unsupervised classification?
How are spectral classes handled in hybrid supervised/unsupervised classification after clustering independently?
How are spectral classes handled in hybrid supervised/unsupervised classification after clustering independently?
What algorithms are commonly used in hybrid supervised/unsupervised classification to classify the entire scene?
What algorithms are commonly used in hybrid supervised/unsupervised classification to classify the entire scene?
How do hybrid supervised/unsupervised classifiers combine training statistics from both approaches?
How do hybrid supervised/unsupervised classifiers combine training statistics from both approaches?
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