<p>Remote sensing change detection often requires dense prediction over wide-area bi-temporal imagery under limited computational budgets. Bi-temporal images frequently contain non-semantic appearance discrepancies from illumination variation, seasonal change, atmospheric effects, and sensor mismatch, which induce pseudo-change responses unrelated to annotated semantic change. Although recent change detection methods employ stronger temporal interaction or global modeling, such methods introduce additional computational cost and slow inference in throughput-sensitive settings. We propose an efficient remote sensing change detection framework based on Adaptive Frequency Masking, which applies learnable branch-specific amplitude and phase masks before shallow temporal differencing to suppress pseudo-change while confining the additional computation to the shallow branch. Experiments on six public benchmarks show that the proposed framework achieves an inference time of 4.13 milliseconds, a throughput of 242.4 frames per second, and 6.05 billion floating-point operations.</p>