| Accuracy Assessment | Remote Sensing | User, Producer and Overall Accuracy | Numerical

| Accuracy Assessment | Remote Sensing | User, Producer and Overall Accuracy | Numerical | CTEVT |

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🅠.🅝. What is Accuracy Assessment ?
✓ Accuracy assessment is the process of evaluating the accuracy of remote sensing data products, such as land cover maps, through comparison with ground truth data. This is important because remote sensing data is not always 100% accurate, and accuracy assessment helps to quantify the level of accuracy and reliability of the data.
User accuracy, producer accuracy, and overall accuracy are three commonly used measures of accuracy in remote sensing:
✓ User Accuracy: User accuracy, also known as commission error, refers to the proportion of correctly classified pixels or areas in a remote sensing image among all the pixels or areas that are classified as a particular land cover class. User accuracy is calculated using the following formula:
User Accuracy = Number of correctly classified pixels in a class / Total number of pixels classified as that class
✓Producer Accuracy: Producer accuracy, also known as omission error, refers to the proportion of correctly classified pixels or areas in a remote sensing image among all the pixels or areas that actually belong to a particular land cover class. Producer accuracy is calculated using the following formula:
Producer Accuracy = Number of correctly classified pixels in a class / Total number of pixels that actually belong to that class
☑️Overall Accuracy: Overall accuracy is a measure of the overall performance of a land cover classification algorithm. It is calculated by comparing the classified map to the reference map, and counting the number of pixels that are correctly classified. Overall accuracy is calculated using the following formula:
Overall Accuracy = (Number of correctly classified pixels / Total number of pixels) x 100%
✔️ In general, high values of user accuracy, producer accuracy, and overall accuracy indicate high levels of accuracy and reliability in the remote sensing data product. However, it is important to note that these measures are not always sufficient for fully evaluating the accuracy of a remote sensing data product, and other measures such as the kappa coefficient may be used in combination with them.
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