Scale selection based on Moran´s I for segmentation of high resolution remotely sensed images

Image segmentation is a prerequisite for object-based image analysis (OBIA). However, selecting an optimal segmentation scale is often time consuming and needs trial-and-error. This paper presents an unsupervised scale selection method based on the rate of change of a spatial autocorrelation indicator - the global Moran´s I for segmentation of high resolution remotely sensed images. It was compared with other two scale selection methods and its effectiveness is validated through both visual analysis and by referencing to multiple manual segmentations. Experimental results on our own data and statistical data from an external reference showed that the optimal scale could be easily selected through the proposed method.