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Questions and Answers
The goal of change detection is to identify the cause of change in the landscape.
The goal of change detection is to identify the cause of change in the landscape.
False
Visual inspection is suitable for detecting subtle changes in land cover.
Visual inspection is suitable for detecting subtle changes in land cover.
False
Post-classification comparison is a method used in multi-temporal image analysis for change detection.
Post-classification comparison is a method used in multi-temporal image analysis for change detection.
True
Change detection is only possible using high-resolution satellite images.
Change detection is only possible using high-resolution satellite images.
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The accuracy of change detection results can be assessed using metrics such as the Kappa Coefficient.
The accuracy of change detection results can be assessed using metrics such as the Kappa Coefficient.
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A Kappa value of 0.70 indicates a very good agreement between the two observers.
A Kappa value of 0.70 indicates a very good agreement between the two observers.
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Change detection methods can only be used to detect long-term changes such as urban growth or desertification.
Change detection methods can only be used to detect long-term changes such as urban growth or desertification.
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When applying Temporal Image Differencing, the values of the change image are typically between 0 and 255
When applying Temporal Image Differencing, the values of the change image are typically between 0 and 255
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Radiometric and atmospheric corrections are not necessary for accurate change detection analysis.
Radiometric and atmospheric corrections are not necessary for accurate change detection analysis.
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A Kappa value of -0.5 implies that the two observers have a perfect agreement.
A Kappa value of -0.5 implies that the two observers have a perfect agreement.
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Temporal Image Differencing using a single band can detect subtle changes in land cover
Temporal Image Differencing using a single band can detect subtle changes in land cover
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Spatial registration of images with an accuracy of 1-2 pixels is sufficient for change detection analysis.
Spatial registration of images with an accuracy of 1-2 pixels is sufficient for change detection analysis.
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Areas of no change in the Temporal Image Differencing method have a value of 0 in the change image
Areas of no change in the Temporal Image Differencing method have a value of 0 in the change image
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The NDVI method of Temporal Image Differencing can be used to detect changes in vegetation health
The NDVI method of Temporal Image Differencing can be used to detect changes in vegetation health
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Temporal Image Differencing using NDVI can be used to classify land cover changes into more than two categories
Temporal Image Differencing using NDVI can be used to classify land cover changes into more than two categories
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The Kappa Coefficient is a measure of the accuracy of change detection results, with values ranging from $0$ to $1$, where $1$ indicates perfect agreement and $0$ indicates no agreement.
The Kappa Coefficient is a measure of the accuracy of change detection results, with values ranging from $0$ to $1$, where $1$ indicates perfect agreement and $0$ indicates no agreement.
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The Maximum Likelihood Classifier is a type of unsupervised classification method.
The Maximum Likelihood Classifier is a type of unsupervised classification method.
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Temporal image differencing is a method of change detection that involves comparing two or more images of the same area taken at different times.
Temporal image differencing is a method of change detection that involves comparing two or more images of the same area taken at different times.
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The confusion matrix is a metric used to assess the accuracy of change detection results, and it provides a summary of the number of true positives, false positives, true negatives, and false negatives.
The confusion matrix is a metric used to assess the accuracy of change detection results, and it provides a summary of the number of true positives, false positives, true negatives, and false negatives.
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The k-Nearest Neighbour Classifier is a type of unsupervised classification method that assigns each pixel to the class of its k-nearest neighbours.
The k-Nearest Neighbour Classifier is a type of unsupervised classification method that assigns each pixel to the class of its k-nearest neighbours.
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