As an unsupervised over-segmentation technique, superpixel decomposition is a popular preprocessing step that partitions a visual image into nonoverlapping regions, thereby improving the overall quality and clarity of the image. Despite various attempts being made to boost superpixel quality, most approaches still suffer from the limitation of conventional grid-sampling based seed initialization, which limits regional representation in several typical applications. In this work, the authors present a new sequential seeding strategy (SSS) to further optimize the segmentation performance of simple noniterative clustering (SNIC) superpixels. First, they employ conventional grid-level sampling (GLS) to establish an MEM by sowing half of the expected seeds. To capture more image details, the authors then sequentially sample new seeds from the midpoint of a pair of seeds that show the strongest correlation, which is quantified by a novel linear-path-based measurement. In this process, they describe the spatial relationship of all seeds via a dynamic region adjacency graph. Compared with the conventional GLS initialization, the SSS takes both the regularity of global distribution and the complexity of local context into consideration. Therefore, it is more accordant with the varying content of an image and suitable for many seed-demand superpixel generation frameworks. Extensive experiments verify that the SSS offers SNIC superpixels considerable improvement on regional description in terms of several quantitative metrics and number controllability. Furthermore, this integrated framework enables multiscale adaptation in generating superpixels as evidenced by its application in airport scene decomposition.