PTF-Net: Pseudo-Temporal Feature Fusion Network for Bi-Temporal Semantic Change Detection

Abstract

Bi-temporal Semantic Change Detection (SCD) is a fundamental analytical framework for capturing state transitions across various domains, with remote sensing being a key application area in this work. However, the prevailing paradigm of comparing two discrete temporal snapshots suffers from inherent limitations due to temporal discontinuity. These limitations lead to pronounced radiometric inconsistencies and pseudo-change noise in Bi-temporal SCD. To address these, we propose the Pseudo-Temporal Feature Fusion Network (PTF-Net), which reconceptualizes bi-temporal SCD as continuous pseudo-temporal evolution modeling. This novel paradigm facilitates the recovery of the missing temporal context between observation points. At its core lies an innovative Non-Linear Style Interpolation Mechanism, which projects discrete bi-temporal observations onto a smooth latent manifold to simulate plausible surface evolution dynamics. The mechanism synthesizes a sequence of semantically coherent intermediate representations, which inherently bridge the temporal gap and disentangles style variations from semantic changes. To fully exploit this synthesized sequence, we design a dedicated Temporal Dynamics Perception Branch. This branch employs efficient temporal interactions to robustly discriminate structural mutations from transient noise, effectively acting as a semantic filter. Extensive experiments on three public datasets reveal that PTF-Net consistently outperforms state-of-the-art methods, achieving comprehensive superiority across mIoU, Fscd, and SeK metrics.