Automatic thresholding abundance fractional images for mixed pixel classification

Mixed pixel classification is different from spatial-based image classification in the sense that the former deals with abundance fractional images resulting from mixed pixels as opposed to classification maps produced by the latter. As a result, mixed pixel classification is generally carried out by visual inspection on the generated abundance fractional images. Consequently, it can be very subjective and vary with different human interpretations. Under such circumstance, it is difficult to substantiate an algorithm and conducting a comparative analysis is impossible. This paper presents one histogram-based approach to thresholding abundance fractional images. It thresholds an abundance fractional image into a binary image using a probability of confidence as a threshold value.