What is the goal of quantitative accuracy assessment?

What is the goal of quantitative accuracy assessment?

The purpose of quantitative accuracy assessment is the identification and measurement of map errors. It involves comparison of a site on a map (‘classified data’) against field or photo information (‘reference data’) for the same site.

How do you do accuracy assessment in erdas?

Performing Accuracy Assessment In Erdas Imagine Open up the image ‘watershed_unsup4. img’ that you created in a viewer. Click on the Raster tab –> Classification –> Supervised –> Accuracy Assessment. A new window will open which is the main window for the accuracy assessment tool.

Why is the assessment of classification required?

Accuracy assessment is an important part of any classification project. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification.

What is quantitative accuracy?

With Quantitative Accuracy study, one can estimate trueness, precision and accuracy of a method. The samples used in the study should represent multiple sample levels, and these concentrations should be known. It is recommended to use both controls and patient samples.

Why is accuracy assessment important in image classification?

Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. The ground truth layer determines the number and placement of the random points according to the sampling strategy.

What is accuracy assessment in erdas?

Accuracy assessment is the procedure used to quantify the reliability of a classified image. The standard accuracy assessment procedure is to construct an “error matrix.” This is a square matrix in which the rows and columns represent the land cover classes from the classified image.

Why is accuracy assessment important for image classification?

Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data.

How to assess the accuracy of a classified map?

The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix.

How to use stratified random for accuracy assessment?

Select a sampling strategy. Random—Generates random accuracy assessment points across the entire input dataset. Stratified Random—Generates a set of accuracy assessment points that is proportional in number to the class area for each class.

How to create an accuracy assessment point table?

Use the output from Create Accuracy Assessment Points tool as the Input Accuracy Assessment Points. Set the Target Field to Classified. The table is updated to include the value of each point based on the classified data.