Semivariogram-Based Spatial Bandwidth Selection for Remote Sensing Image Segmentation With Mean-Shift Algorithm

Abstract

Image segmentation is a key procedure that partitions an image into homogeneous parcels in object-based image analysis (OBIA). Scale selection in image segmentation is always difficult for high-performance OBIA. This letter is aimed at scale selection before segmentation in OBIA and proposes a spatial statistics-based spatial bandwidth selection method based on mean-shift segmentation. This study uses Ikonos and Quickbird panchromatic images as the experimental data and then computes their semivariances to select the optimal spatial bandwidth for mean-shift segmentation. To validate this method and interpret the relationship between the semivariances and segmentation scale, this letter implements an image segmentation evaluation based on the homogeneity within and the heterogeneity between the segmentation parcels. The evaluation results basically support the proposed scale selection method based on the semivariogram. Consequently, the semivariogram-based spatial bandwidth selection method is practically meaningful for pre-estimating the appropriate scale and thus contributes to improving the performance and efficiency of OBIA.