Application of equivocation quantification to fuzzy data

Considers multispectral remote sensing images where every pixel in the data corresponds to a mixed pattern on the ground. In the image processing it is necessary to consider one pixel as one pattern on the ground in one case, and one pixel as multi patterns in another case. The former is used to calculate the classification scores in order to assess classification results. Since the classification score is calculated based on crisp data, the original equivocation quantification can be used to assess the classification result. However, since the later one is fuzzy data, i.e., the membership function value is a real number from zero to one, the original equivocation quantification cannot be used to assess the classification result directly. The authors introduce a new concept called pseudo-probability to adapt fuzzy data to calculating the equivocation quantification.