Enhancing DeepLabv3+ With Multiscale Sparse-Aware Dilated Convolution and Boundary-Interactive ASPP for River Ice Segmentation

Accurate segmentation of river ice in remote sensing imagery remains challenging due to heterogeneous ice textures, fragmented floes, and ambiguous ice–water boundaries. While DeepLabv3+ effectively captures multi-scale context, its standard atrous convolution suffers from uniform sampling and gridding effects, which limit boundary discrimination in complex river ice scenes. To address these issues, we propose a lightweight enhancement to DeepLabv3+ that integrates regionally weighted dilated convolution, a coordinated dilation configuration strategy, and a boundary-aware refinement branch. The proposed design improves central feature aggregation while preserving global context, without introducing additional computational overhead. Experiments on the NWPU_YRCC2 dataset demonstrate a 1.95% mIoU improvement over the baseline DeepLabv3+, validating the effectiveness of the proposed framework for precise and efficient river ice segmentation.