Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran’s I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.