MCQ_Lecture 5_Remote Sensing Image Interpretation

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Questions and Answers

What is the primary objective of image classification in remote sensing?

To identify the spatial resolution of an image

To visualize the data in 3D format

To automatically categorize all pixels using numerical spectral patterns (correct)

To reduce the noise in the image

Which of the following supervised classification methods is based on the concept of minimum distance?

Support Vector Machine (SVM)

k-Nearest Neighbour Classifier (k-NN)

Minimum Distance to Means Classifier (MDM) (correct)

Maximum Likelihood Classifier (MLC)

What is the primary difference between supervised and unsupervised classification?

Supervised classification is more complex than unsupervised classification

Supervised classification uses more bands than unsupervised classification

Unsupervised classification is more accurate than supervised classification

Supervised classification uses training data, while unsupervised classification does not (correct)

Which of the following is NOT a type of unsupervised classification method?

Maximum Likelihood Classifier (MLC) Signup and view all the answers

What is the primary purpose of accuracy assessment in remote sensing?

To evaluate the accuracy of the classification model Signup and view all the answers

What is the primary characteristic of a pixel-by-pixel operation in image classification?

Every pixel is classified individually Signup and view all the answers

What is the purpose of a scattergram in supervised classification?

To visualize the spectral ranges of training areas Signup and view all the answers

What is the advantage of the Minimum Distance to Means Classifier (MDM)?

It is insensitive to different degrees of variance Signup and view all the answers

What is the difference between k=1 and k>1 in the k-Nearest Neighbour classifier?

k=1 has a lower training error rate Signup and view all the answers

What happens when an unknown pixel is farther than a defined distance in the Minimum Distance to Means Classifier?

It is classified as unknown Signup and view all the answers

What is the primary objective of creating training areas in supervised classification?

To assemble a set of descriptive statistics Signup and view all the answers

What is the primary advantage of using machine learning in remote sensing image classification?

To enable automated detection of patterns in large datasets Signup and view all the answers

What is the significance of coincident spectral plots in supervised classification?

To examine the overlap between optical bands and thermal bands Signup and view all the answers

What is the primary distinction between parametric and non-parametric classification methods?

The assumption of data distribution Signup and view all the answers

What is the result of the classification output in a GIS?

Production of thematic maps, tables, or statistics Signup and view all the answers

What is the primary goal of the training stage in supervised classification?

To identify the optimal classification parameters Signup and view all the answers

Why is it essential to represent all spectral classes constituting each information class in the training set statistics?

To ensure accurate classification of each pixel in the dataset Signup and view all the answers

Which of the following classification methods is typically used for land cover classification?

Hard per-pixel classification Signup and view all the answers

What is the primary difference between supervised classification and unsupervised classification?

The presence or absence of training areas Signup and view all the answers

What is the primary advantage of using hybrid classification methods?

Flexibility to integrate different classification approaches Signup and view all the answers

Study Notes

Image Classification & Machine Learning

Image classification is a process of automatically categorizing all pixels in an image using numerical spectral patterns for each pixel.
It uses more than one band; otherwise, it's called density slice, which relies only on brightness.
The objective is to categorize pixels into land-use/land-cover classes.

Supervised Classification

k-Nearest Neighbour Classifier (k-NN):
- Uses Euclidean distance to find k nearest neighbours to a query point.
- Classifies using a majority vote among the k neighbours.
- If k=1, training error rate is 0, but test error may be high.
Minimum Distance to Means Classifier (MDM):
- Classified by minimum distance between an unknown pixel and each category mean.
- May define a distance; if unknown pixel is farther, classified as unknown.
- Insensitive to different degrees of variance.
Maximum Likelihood Classifier (MLC):
- (No detailed information provided in the text)

Training Stage in Supervised Classification

Training stage: analyst identifies representative training areas and develops numerical descriptions of spectral attributes for each cover type.
Analyst must develop training statistics for all spectral classes constituting each information class to be discriminated by the classifier.
Training areas must be representative and complete.

Training Area Creation and Statistics

Main objective: assemble a set of descriptive statistics.
Training areas must be representative and complete (rooftop segmentation example).
All spectral classes constituting each information class must be adequately represented in the training set statistics.

Classification Stage

Each pixel in the dataset is categorized into the land cover class it most closely resembles.
Learn from data, then predict what we want to know.

Land Use and Land Cover

Land use refers to what people do on the land surface (e.g., agriculture, commerce, settlement).
Land cover refers to the type of material present on the landscape (e.g., water, sand, crops, forest, wetland, human-made materials).

Machine Learning

Machine learning is a set of methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data or perform other kinds of decision making under uncertainty.
Supervised learning and unsupervised learning are two types of machine learning.

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Description

Test your knowledge of remote sensing image interpretation, including image classification and machine learning concepts, supervised and unsupervised classification methods, and more. Based on Lecture 5 of Dr. Zhiwei Li's course.