Robust Segmentation of Mangrove in Remote Sensing Images via ODE-Based Neural Networks and Adversarial Training

Mangrove ecosystems are recognized for their exceptional carbon sequestration potential and crucial contribution to coastal ecological balance. However, the sharp decline in mangrove area necessitates efficient monitoring via remote sensing. While Deep Neural Networks (DNNs) have excelled in segmentation tasks, their robustness remains inadequate. This limitation stems from the lack of theoretical guarantees regarding the continuity of layer-by-layer discrete transformations, rendering models susceptible not only to man-made adversarial attacks but also to natural degradations. To address these vulnerabilities, this paper leverages Neural Ordinary Differential Equations (NODEs) to enhance the robustness of mangrove segmentation. We designed and integrated various NODE architectures, including a novel NODE-SE-Block inspired by adaptive feature recalibration, to achieve more stable feature representations. Crucially, our findings reveal that by employing an adversarial training framework based on known attacks, the NODE-integrated network demonstrates superior cross-domain robustness. It not only defends against malicious exploits but also exhibits significantly enhanced resilience toward natural degradations, such as Gaussian noise and sensor-induced artifacts. Experimental results on mangrove datasets verify that the proposed methodology provides a reliable and interference-resistant foundation for ecological management in mission-critical scenarios.