Proceedings

D2MDM2: A Brain-Inspired Deep Network Based on DDM Decision-Making Mechanism for Remote Sensing Change Detection

Kang Zhao, Ye Zhang, Bin Wang, Jianchao Zeng

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2118-2126.

https://doi.org/10.24963/ijcai.2026/236

Keywords

  • Computer Vision: Recognition (object detection, categorization)
  • Computer Vision: Adversarial learning, adversarial attack and defense methods
  • Humans and AI: Brain sciences

Abstract

Remote sensing change detection (RSCD) aims to identify changed regions in bitemporal images. However, conventional one-step modeling suffers from performance degradation caused by imaging temporal differences (e.g., illumination disturbances, seasonal variations). To address this issue, we formulate the problem as an “adversarial attack and defense” paradigm. Then, instead of traditional adversarial training, we propose a brain-inspired deep network based on the drift diffusion model (DDM) decision-making mechanism for RSCD, which is rooted in cognitive neuroscience. By simulating brain decision processes, the network enhances model robustness significantly. Specifically, we design the multi-stage reasoning unit to simulate DDM’s iterative evidence generation, stochastic representation unit with reparameterization sampling to model stochastic diffusion in decision-making, decision aggregation unit to replicate DDM’s evidence accumulation, and loss-guided unit with multi-loss joint constraints to simulate directional drift. Experimental results verify the model’s superior performance on multiple datasets and their perturbed versions.

Kang Zhao, Ye Zhang, Bin Wang, and Jianchao Zeng. 2026. D2MDM2: A Brain-Inspired Deep Network Based on DDM Decision-Making Mechanism for Remote Sensing Change Detection. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2118–2126. https://doi.org/10.24963/ijcai.2026/236
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