Optimal Segmentation Scale Selection for Object-Based Change Detection in Remote Sensing Images Using Kullback–Leibler Divergence

Abstract

The scale parameter (SP), to control the sizes of objects, is of great significance in multiscale segmentation which is a prerequisite and foundational step for object-based change detection (OBCD). However, the appropriate SP is not readily apparent and the majority of the existing OBCD algorithms obtain the SPs by empirical or subjective trial-and-error ways that may lead to dissatisfactory accuracy and be time-consuming. To address this issue, an automatic approach for optimal segmentation scale selection for OBCD is proposed in this letter. First, a changed fuzziness image for bitemporal images was generated. Second, multiscale segmentation was implemented in a series of candidate scales, and the merging relationships between adjacent scales were built. Then, mapping the segments to the previous fuzziness image, a statistical metric describing the homogeneity of objects based on the Kullback-Leibler divergence was defined, the increments of the metric between merged objects and their child objects were calculated and weighted to identify the optimal SPs. For performance evaluation, Dempter-Shafer (DS) evidence fusion was utilized in the scales selected by the proposed approach in comparison with other state of art or empirical ones. The experimental results employing GF-1, Google Earth, and aerial images demonstrated the superiority and effectiveness of the SPs identified by the proposed approach in the OBCD task.