Segmentation optimization via recognition of the PSE-NSR-ED2 patterns along with the scale parameter in object-based image analysis

Abstract

To create image objects for subsequent classification in object-based image analysis, an optimal segmentation threshold (OST) is a pre-requisite for image segmentation. However, an OST is practically acquired by assessing and ranking an exhaustive segment data stack constructed after foregoing image segmentation. In this paper, we propose an iterative exploration method via recognition of the Euclidean distance 2 (ED2)–scale parameter (SP) pattern with the least five tiles of segment data stack in each cycle among the Potential Segmentation Error (PSE)–Number-of-Segments Ratio (NSR)–ED2 patterns along with SP. We conducted two experiments. In the first experiment, we validated the general italic U-shaped ED2–SP pattern by constructing exhaustive segment data stacks and corresponding segment data stacks. In the second experiment, we adopted the proposed iterative exploration method for OST selection based on the ED2–SP pattern with respect to five equal-spacing SPs in each cycle. The bottom of the pattern was persistently approached by constructing updated segment data stacks and corresponding segment data stacks with five dynamically adjusted tiles. Our results showed that the PSE–NSR–ED2 discrepancy measure system is advantageous to OST selection.