A New Change Detection Method for Heterogeneous Remote Sensing Images Via an Automatic Differentiable Adversarial Search

Highlights: What are the main findings? This paper designs a heterogeneous change detection method based on a neural architecture search, which builds an adaptive discriminator via a differential filter-based composition search (DFCS) strategy. A Gabor filter and local normalized cross-correlation (G-LNCC)-based module is designed for robust cross-modal feature alignment at the input stage. What are the implications of the main findings? The introduction of the Differentiable Neural Architecture Search (NAS) process offers a novel and adaptive paradigm for the Hete-CD field, overcoming the limitations of fixed deep network architectures. The model demonstrates strong generalizability and dynamic learning capabilities in complex Hete-CD tasks. Heterogeneous remote sensing image change detection (Hete-CD) holds significant research value in military and civilian fields. The existing methods often rely on expert experience to design fixed deep network architectures for cross-modal feature alignment and fusion purposes. However, when faced with diverse land cover types, these methods often lead to blurred change boundaries and structural distortions, resulting in significant performance degradations. To address this, we propose an adaptive adversarial learning-based heterogeneous remote sensing image change detection method based on the differentiable filter combination search (DFCS) strategy to provide enhanced generalizability and dynamic learning capabilities for diverse scenarios. First, a fully reconfigurable self-learning discriminator is designed to dynamically synthesize the optimal convolutional architecture from a library of atomic filters containing basic operators. This provides highly adaptive adversarial supervision to the generator, enabling joint dynamic learning between the generator and discriminator. To further mitigate modality differences in the input stage, we integrate a feature fusion module based on the Gabor and local normalized cross-correlation (G-LNCC) to extract modality-invariant texture and structure features. Finally, a geometric structure-based collaborative supervision (GSCS) loss function is constructed to impose fine-grained constraints on the change map from the perspectives of regions, boundaries, and structures, thereby enforcing physical properties. Comparative experimental results obtained on five public Hete-CD datasets show that our method achieves the best F1 values and overall accuracy levels, especially on the Gloucester I and Gloucester II datasets, achieving F1 scores of 93.7% and 95.0%, respectively, demonstrating the strong generalizability of our method in complex scenarios.