# Dual-Stream Difference Modeling with Deep-Guided Multiscale Fusion for Mangrove Change Detection

## TL;DR

This paper introduces a new deep learning method for detecting changes in mangrove ecosystems, which improves accuracy in challenging coastal environments affected by tides.

## Contribution

The novel DSDGMNet method combines dual-stream difference modeling and deep-guided multiscale fusion to better detect true mangrove changes despite tidal interference.

## Key findings

- DSDGMNet achieved an F1-score of 71.36% on the GBCNR dataset, outperforming existing methods like SNUNet and ChangeFormer.
- On the WHU-CD dataset, DSDGMNet scored 91.38%, surpassing DDLNet and ChangeFormer in detecting mangrove changes.

## Abstract

Accurate mangrove change detection is important for coastal ecosystem monitoring but remains challenging due to tidal disturbances, unstable land–water boundaries, and multi-scale distribution variability. Tidal fluctuations introduce spectral variations that obscure real changes. As a result, existing deep learning methods face difficulties in distinguishing tide-induced pseudo-changes while balancing semantic consistency and boundary accuracy. To address these issues, we propose DSDGMNet, which incorporates Dual-Stream Difference Modeling and Deep-Guided Multiscale Fusion. The dual-stream difference-driven strategy is designed to reduce tidal interference and improve sensitivity to true structural changes, and the deep-guided multiscale fusion module integrates global context with fine boundary details. Experiments on the GBCNR dataset show that DSDGMNet achieves an F1-score of 71.36% compared to 68.87% by SNUNet (Siamese Densely Connected UNet) and 66.39% by ChangeFormer. On the WHU-CD dataset, DSDGMNet yields an F1-score of 91.38%, in comparison with 89.85% for DDLNet and 88.82% for ChangeFormer. These results suggest the method’s effectiveness for mangrove change detection in complex intertidal environments.