Remote Sensing - Image Classification

Questions and Answers

What is Image Classification?

Classifying pixels based on user-defined themes

Grouping of similar pixels based on spectral characters (correct)

Classifying pixels without human interaction

Assigning pixels based on textural information

What are Point/Spectral Classifiers used for?

Classifies based on spectral patterns recognized

What improves the accuracy of Neighbourhood/Spatial Classifiers?

Higher accuracy than Spectral Classifiers

What does Feature Space represent?

Plotting reflectances as x;y scatter plots Signup and view all the answers

What defines Informational Classes?

Classes defined by and of interest to the user Signup and view all the answers

What are Spectral Classes?

Groups of uniform pixels Signup and view all the answers

What characterizes Unsupervised Classification?

Minimal user interaction during the classification process Signup and view all the answers

What is ISODATA?

Iterative Self-Organising Data Analysis Technique Signup and view all the answers

What is Supervised Classification?

Operator defines classes during the training process Signup and view all the answers

What are Training Areas?

Sample sites representative of clusters collected during training Signup and view all the answers

What is a Parametric Signature?

Signature based on statistical parameters Signup and view all the answers

Describe a Nonparametric Signature.

Signature based on Area Of Interest defined in feature space Signup and view all the answers

What is the Parallelepiped Classifier?

Simplest classifier that labels pixels according to defined classes Signup and view all the answers

What does Minimum Distance to Mean calculate?

Calculates the Euclidean distances of training data to form cluster centers Signup and view all the answers

What is the Mahalanobis Distance?

Calculates distance from the mean by weighting the differences Signup and view all the answers

How does Maximum Likelihood function?

Estimates means and variances of classes to assign pixels based on probabilities Signup and view all the answers

What is Hybrid Classification?

Use output from unsupervised to train supervised classification Signup and view all the answers

Study Notes

Image Classification

Assigns pixels to specific classes or themes based on spectral characteristics.
Groups similar pixels by analyzing their reflectance patterns across different spectral bands.

Point / Spectral Classifiers

Classify individual pixels based on recognized spectral patterns.
Limited in identifying relationships distinguishable to the human eye.

Neighbourhood / Spatial Classifiers

Utilize both spectral and textural information for classification.
Provide higher accuracy compared to spectral classifiers.

Feature Space

Reflectances are plotted as x; y scatter plots.
Distances between classes in feature space are crucial for distinguishing classes.

Informational Classes

User-defined classes based on specific interests and objectives.
Characteristics such as brightness, topography, color, and texture are considered.

Spectral Classes

Comprised of uniform pixels based on brightness across multiple channels.
Spectral classes contribute to the formation of informational classes.

Unsupervised Classification

Minimal user interaction; spectral classes are grouped initially.
Algorithms like K-Means and ISODATA identify natural groupings.
Analyst defines the number of classes and separation, reducing human error risks.
Cannot align with informational classes; requires additional processing.

ISODATA

An iterative self-organizing data analysis technique for unsupervised classification.
Pixels are grouped around centroids and reclassified based on new centroid positions.
Iterations can be limited or terminated upon classification stability.

Supervised Classification

Operator defines classes during a training process, relying on spectral information.
Each pixel is assigned to the class it most closely resembles.
Can identify classification errors but may lack representative training areas.

Training Areas

Representative sample sites used for comparison against classification algorithms.
Recommend using over 100 pixels and 5-10 training areas per class.
Need for uniformity and homogeneity in training areas.

Parametric Signature

Based on statistical parameters of training areas (min, max, mean).
Decision Rules include Mahalanobis Distance, Minimum Distance to Mean, and Maximum Likelihood.

Nonparametric Signature

Based on defined Areas of Interest in feature space.
Pixels assigned based on their position within the feature space using Parallelepiped decision rules.

Parallelepiped

Simplistic classifier that assigns pixels to defined classes based on training data.
Unclassified pixels are labeled as "unknown," leading to potential class overlaps.

Minimum Distance to Mean

Measures Euclidean distances from training data to cluster centers (mean).
Pixels assigned to the nearest cluster but may overlook variability, risking overlap.

Mahalanobis Distance

Accounts for variations among samples by applying a co-variance matrix.
Estimates distances from the mean while assuming normal distribution; more computation-intensive.

Maximum Likelihood

Utilizes training data to estimate means and variances for probability assignments.
Pixels classified in line with the highest probability; assumes equal class probabilities.

Hybrid Classification

Combines outputs from unsupervised to train supervised classification models.
An approach to leverage advantages of both classification types for improved accuracy.

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Description

Explore the essential concepts of image classification in remote sensing through this set of flashcards. Learn about assigning pixels to classes and the use of spectral classifiers to identify pixel patterns. Perfect for students and professionals looking to enhance their knowledge in this critical area of environmental science.