| Calculate Error of Commission and Error of Commission | Producers, User and Overall Accuracy |

🅠.🅝. 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.
✓ Commission error and omission error are two types of errors that can occur during accuracy assessment in remote sensing.
✓ Commission error occurs when a feature is incorrectly classified as present in the image when it is not actually present on the ground. In other words, commission error is the error of commission, or false positive errors. This can occur due to various reasons such as confusion with similar features, classification algorithm errors, or low quality of the data.
✓ On the other hand, omission error occurs when a feature is present on the ground, but it is not detected or classified in the image. In other words, omission error is the error of omission, or false negative errors. This can occur due to various reasons such as low spatial resolution of the data, presence of shadows or cloud cover, or inadequate spectral information.
✓ Both commission and omission errors can affect the accuracy assessment of remote sensing data and can lead to inaccurate results. Therefore, it is important to minimize these errors as much as possible by using appropriate techniques for data acquisition, processing, and analysis.
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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