LiEAF-Net: A Lightweight Multiscale Elevation-Aware Fusion Network for Multimodal Semantic Segmentation of High-Resolution Remote Sensing Imagery

Abstract

Semantic segmentation of high-resolution remote sensing imagery is computationally demanding, particularly for multimodal fusion of RGB and normalized digital surface model (nDSM) data. Existing multimodal networks improve segmentation accuracy but often introduce substantial computational overhead. This letter presents LiEAF-Net, a lightweight elevation-aware fusion network with only 6.50M parameters. The proposed elevation-aware double-attention selective kernel (EADASK) module enables effective cross-modal feature interaction through SE-based calibration, modality-specific multiscale feature extraction, and dual spatial–channel attention. Experiments on the ISPRS Vaihingen and Potsdam datasets achieve 83.59% and 86.45% mIoU, respectively, with 4.8– $35.7\times $ fewer parameters than state-of-the-art multimodal methods, demonstrating the efficiency of LiEAF-Net. The source code is publicly available at https://github.com/sultonouv/lieafnet