Calculation of the optimal segmentation scale in object-based multiresolution segmentation based on the scene complexity of high-resolution remote sensing images

Abstract

The quality of multiresolution segmentation directly influences the accuracy of high-resolution remote sensing image classification using object-oriented analysis technology. However, a perfect segmentation scale optimization method has not yet been developed. Using the fact that the optimal segmentation scale of high-resolution remote sensing images is closely related to the complexity of the objects on the image, we propose an approach for calculating the optimal segmentation scale based on the scene complexity of an image. First, we calculate the scene complexity of high-resolution remote sensing images using Watson’s vision model. Then, we analyze the relationship between the image scene complexity and the optimal segmentation scale based on the model calculation. Optimal segmentation scales are found to be related to the scene complexity of high-resolution remote sensing images by an exponential function, allowing direct calculation of the optimal segmentation scale based on the fitted formulas and the image scene complexity. Finally, we propose a multilevel segmentation strategy to increase the object targeting in the optimal segmentation scale. The optimal segmentation scale calculation method proposed here is simple to perform and has a broad range of potential applications.