CRMF-Net: A Multimodal Fusion Network for Water–Land Classification From Single-Wavelength Bathymetric LiDAR

Accurate water-land classification is fundamental for topographic mapping and coastal zone monitoring based on airborne LiDAR bathymetry (ALB). However, due to the limited information content and feature ambiguity of one-dimensional waveform signals, accurate classification from single-wavelength ALB data remains challenging. To address this issue, a dual-branch multimodal fusion network (CRMF-Net) is proposed to improve both classification accuracy and robustness. The proposed network consists of a Convolutional Neural Network (CNN) branch and a Convolutional Block Attention Module optimized Residual Neural Network (ResNet-CBAM) branch, which are designed to capture complementary temporal and spatial features, respectively. The one-dimensional green waveform is converted into a two-dimensional time-frequency representation through the Continuous Wavelet Transform (CWT), thereby increasing the dimensions and quantity of waveform features. By jointly exploiting complementary information from waveform signals and their corresponding time–frequency representations, the proposed method enables more effective feature representation without relying on extensive handcrafted analysis. Experiments conducted on CZMIL datasets from Qinshan Island demonstrate that CRMF-Net achieves an overall accuracy of 97.33% with a kappa coefficient of 0.9168, outperforming traditional methods such as Fuzzy C-Means (FCM), Support Vector Machine (SVM), and the 1D-CNN approach. These results indicate that the proposed method provides a promising solution for fully automated processing of single-wavelength ALB data.