Superpixel segmentation provides a way to capture object boundaries unsupervised and has benefited many compute vision applications. However, under-segmentation for weak boundaries and poor compatibility with image feature representations often limit its wide application. In this paper, we propose a new weak-boundary sensitive superpixel generation method and provide an all-in-one solution for images with different feature representations. We first design a local adaptive distance (LAD) to be more sensitive to feature changes in low-contrast regions. LAD leverages image local standard deviation as region contrast clues. It adaptively increases the feature distances in low-contrast regions to avoid feature space distances of weak boundaries being inundated by regularity constraints. LAD is scale-invariant that can be compatible with high bit-depth and multi-feature images. Then, based on LAD, we introduce a novel morphological contour evolution model to generate superpixels iteratively. Leveraging morphological dilation of superpixel shapes, the new model is more conducive to the boundary detection of irregular or slender objects. Extensive experiments demonstrate that our method favorably outperforms state-of-the-art methods, especially regarding the under-segmentation error and segmentation accuracy.