DPVCD ‐Net: A Difference‐Prior‐Guided Pseudo‐Video Network for Change Detection of Heterogeneous Targets in Complex Remote Sensing Scenes

ABSTRACT

Bi‐temporal remote sensing change detection remains constrained by two major challenges: pseudo‐change interference in complex scenes, which becomes more pronounced when a single model handles urban and mountainous scenes, and the substantial morphological disparity between compact building changes and large‐scale irregular landslide changes. To address these challenges, we propose DPVCD‐Net, a Difference‐Prior‐Guided Pseudo‐Video Change Detection Network. DPVCD‐Net reformulates bi‐temporal images as a pseudo‐video sequence and introduces a learnable perceptual frame to establish an early difference prior for subsequent feature learning. The network integrates two complementary mechanisms: difference guidance and prior fusion. Specifically, DP‐CSA derives channel‐wise and spatial attention from the perceptual feature and residually modulates the complete spatiotemporal feature set, strengthening genuine‐change responses and suppressing pseudo‐changes across different scenes. Meanwhile, PF‐STM (Prior‐Fused Spatiotemporal Multi‐Scale Modeling) consists of two sequential stages: ST‐MDCM (Spatiotemporal Multi‐scale Dilated Convolution Module) performs within‐scale multi‐receptive‐field spatiotemporal aggregation, while EA‐CSCA (Edge‐Aware Cross‐Scale Cross‐Attention module) introduces boundary‐aware cross‐scale interaction for structural refinement. Their sequential coupling strengthens multi‐scale and structural modeling for morphologically disparate change targets. Experimental results on the GVLM‐CD, WHU‐CD, and LBFD‐CD datasets show that DPVCD‐Net achieves F1 scores of 0.9101, 0.9275, and 0.9089, respectively, supporting its effectiveness in suppressing scene‐dependent pseudo‐change interference and performing unified detection of morphologically disparate building and landslide changes within a single shared‐parameter model.