MARC-Net: A Modality-Availability-Aware Robust Change Network for Missing-Optical Bi-Temporal Optical–SAR Change Detection of Reclaimed Cropland

Abstract

Reliable monitoring of reclaimed cropland is hindered when one optical acquisition is unavailable or degraded. We formulate missing-modality bi-temporal optical–SAR change detection and propose the Modality-Availability-Aware Robust Change Network (MARC-Net), a new architecture that combines explicit availability conditioning, condition-aware temporal proxy stabilization, a shared residual input adapter, multi-level signed temporal interaction, dilated context refinement, and hierarchical change decoding. A two-phase condition-balanced learning strategy jointly develops mixed-missing representations and optimizes Full, Missing-O, and Missing-S behavior without reconstructing the unavailable image. On four reclaimed-cropland scenes, the final model obtains IoUs of 0.7826, 0.7039, and 0.7780, respectively, with a three-condition mean of 0.7548. It exceeds the strongest evaluated external baseline mean (0.7381) while using one checkpoint and a fixed argmax decision rule. LOSO and controlled optical-degradation experiments further characterize robustness under geographic shift and progressive observation-quality degradation. These results demonstrate that availability-aware temporal stabilization and condition-balanced optimization provide an effective operating point for incomplete-input reclaimed-cropland monitoring while preserving complete-input performance.