Which tool assesses the accuracy of image classification based on ground truths?

The correct answer is the Confusion Matrix, a fundamental tool used to assess the accuracy of image classification. It compares the classified image results with ground truth data to evaluate how well the classification algorithm performed.

In a confusion matrix, each row represents the instances in a predicted class while each column represents the instances in an actual class. This allows for calculating various accuracy metrics such as overall accuracy, producer's accuracy, user's accuracy, and Kappa coefficient. These metrics provide insight into how often the classified outcomes correspond to the actual classes, helping to identify areas where the classification might be over or under-representing certain classes.

This method is particularly valuable in remote sensing and GIS applications, where classes may represent different land covers or features on the earth's surface. By utilizing ground truth data, the confusion matrix provides a detailed way to reveal the strengths and weaknesses of a classification model.