Finding Thresholds for Image Segmentation

Segmentation methods for images often have cost functions which evaluate the (dis)similarity between pixels or segments. Thresholds on cost values are then used to decide whether or not to grow, join or split segments. The results for a given image critically depend on the selection of the threshold values. In remote sensing, a too low threshold will split up regions of constant ground cover and a too high threshold will join adjacent regions of diierent ground cover. Optimal thresholds can be determined using diierent classes of methods: generating cost value distributions from the original image. obtaining statistical distributions from segmented images. comparing a "true" segmentation with the results of segmentation using a range of thresholds. A so-called "true" segmentation can be derived from human expert segmentations or from maps obtained by ground surveys or segmentation of higher resolution images. Several methods for threshold determination are described for a hybrid segmentation method developed by us. Measures are described for comparison of two segmentations. Results are evaluated using several real and artiicially generated images.