Proposal of a weighted index for segmentation evaluation

Although segmentation is an important process in image classification, selecting among different segmentors and their parameters is a difficult task. This work proposes a reference free index that returns the quality of segmentation, considering the classes that the user intends to classify. Considering a gaussian distribution, this index was tested to evaluate segmentations of an optical simulated image, a LANDSAT5/TM image in a Brazilian Amazon area and its derived fraction image. Index results presented higher values for segmentations more similar to the reference image, and also good agreement with overall accuracy values when classifying the images.