Remote Sensing Image Classification

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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'?

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?

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?

Variability of spectral properties within cover classes Signup and view all the answers

Why are pixel observations from areas of known cover type used as 'training sets'?

To classify pixels into their appropriate classes Signup and view all the answers

What is the main advantage of using multidimensional descriptions of spectral response patterns in classification strategies?

Improved accuracy in classification Signup and view all the answers

What is the first stage in a typical supervised classification procedure?

Training Signup and view all the answers

How are pixels classified in the classification stage of a supervised classification procedure?

Pixel-by-pixel operation Signup and view all the answers

What label is assigned to a pixel that is insufficiently similar to any training dataset during classification?

Unknown Signup and view all the answers

What type of products are typically generated as outputs of the supervised classification process?

Thematic maps, tables of statistics, digital data files Signup and view all the answers

What happens if a pixel is categorized into a land cover class it most closely resembles during classification?

It is assigned that particular class Signup and view all the answers

How do spectral response patterns play a role in image classification?

They form the basis for image classification if distinct for each feature type Signup and view all the answers

What is one advantage of using supervised training data in ISODATA?

Improves the accuracy of the initial unsupervised classification Signup and view all the answers

Why might a multistage approach be used with unsupervised classification?

To focus on a particular class of interest Signup and view all the answers

What is the purpose of masking the 'problem' class in the second stage of classification?

To remove all spectral classes except the 'problem' class Signup and view all the answers

How does the second-stage classification step contribute to the overall process?

It improves the representation of the 'problem' class Signup and view all the answers

What happens during the recoding output from the second-stage classification?

Spectral subclasses are reassigned to existing or new classes Signup and view all the answers

Why is the merger of classification results important in this approach?

To finalize the clustering process Signup and view all the answers

What is the primary function of unsupervised classifiers in pixel classification?

Group pixels based on spectral values without training data Signup and view all the answers

How does the Bayesian classifier differ from maximum likelihood classification?

Incorporates prior probabilities and misclassification costs Signup and view all the answers

What technique is employed to reduce computational complexity in maximum likelihood classification?

Principal component transformations Signup and view all the answers

Which clustering algorithm requires the analyst to specify the number of clusters?

ISODATA Signup and view all the answers

What is the main challenge of maximum likelihood classification for datasets with many spectral channels or classes?

Requires significant computational resources Signup and view all the answers

What do unsupervised classifiers initially classify pixels as?

"Unknown" Signup and view all the answers

What is the main objective of hybrid supervised/unsupervised classification methods?

To enhance the accuracy of purely supervised or unsupervised procedures Signup and view all the answers

In hybrid supervised/unsupervised classification, why are unsupervised training areas intentionally selected?

To identify numerous spectral classes needed for supervised classification Signup and view all the answers

What is the role of guided clustering in hybrid supervised/unsupervised classification?

To effectively handle complex spectral variability within cover types Signup and view all the answers

How are spectral classes handled in hybrid supervised/unsupervised classification after clustering independently?

Combined if similar and subjected to pooled statistical analysis Signup and view all the answers

What algorithms are commonly used in hybrid supervised/unsupervised classification to classify the entire scene?

Minimum distance and maximum likelihood Signup and view all the answers

How do hybrid supervised/unsupervised classifiers combine training statistics from both approaches?

By combining statistics to classify the entire scene Signup and view all the answers