Abstract
The goal of this work is to present a set of statistical tests that offer a formal procedure to make a decision as to whether a set of thematic quality specifications of a product is fulfilled within the philosophy of a quality control process. The tests can be applied to classification data in thematic quality control, in order to check if they are compliant with a set of specifications for correctly classified elements (e.g., at least 90% classification correctness for category A) and maximum levels of poor quality for confused elements (e.g., at most 5% of confusion is allowed between categories A and B). To achieve this objective, an accurate reference is needed. This premise entails changes in the distributional hypothesis over the classification data from a statistical point of view. Four statistical tests based on the binomial, chi-square, and multinomial distributions are stated, to provide a range of tests for controlling the quality of product per class, both categorically and globally. The proposal is illustrated with a complete example. Finally, a guide is provided to clarify the use of each test, as well as their pros and cons.
Highlights
The thematic component of a spatial data product is expressed as a set of classes, or category assignments
Remote Sens. 2020, 12, 816 classified elements, and (ii) maximum levels of poor quality for confused elements
The first is focused on the results of the example that has been presented, and the second mainly focuses on comparing the proposal of tests based on the Quality Control Column Set (QCCS) with the methods and indices based on the whole confusion matrix; in this case, the argument is qualitative, as methods based on the confusion matrix cannot be applied to the presented example
Summary
The thematic component of a spatial data product is expressed as a set of classes, or category assignments (e.g., land-cover and land-use classes, geological and pedological classes, and so on). This thematic component of spatial data is of great importance in environmental modeling, decision making, climate change assessment, and so on. A classification scheme has two critical components [7]: (i) a set of labels, and (ii) a set of rules for assigning labels In this way, in the case of a crisp classification, a unique assignment of classes is achieved if the classes (labels) are mutually exclusive (there are no overlaps) and exhaustive (there are no omissions or commissions of classes)
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