An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation

Image segmentation is a critical step in the analysis of high-spatial-resolution remotely sensed imagery using object-based image analysis. The segmentation quality is extremely important to the subsequent analysis. This letter proposes an improved unsupervised method to evaluate the segmentation quality for remotely sensed imagery. The evaluation criteria take into account global intrasegment homogeneity and intersegment heterogeneity measures, which can be useful for the comparison of segmentation results produced by a single segmentation method. The proposed method is compared with other two mature unsupervised evaluation methods on two segmentation methods: region growing and mean shift. QuickBird images are used for the comparative study. The effectiveness of the proposed method is validated through comparing with the supervised evaluation method Rand Index and visual analysis.