FIF-Net: feature interaction and fusion network for multiclass object change detection

doi:10.1080/10095020.2026.2675167

FIF-Net: feature interaction and fusion network for multiclass object change detection

2026-05-22

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DOI：10.1080/10095020.2026.2675167

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Multiclass Object Change Detection (MCOCD) has emerged as a pivotal task in remote sensing image analysis, detecting the locations, categories, and change types (disappeared, unchanged, appeared) of multiclass, time-sensitive objects across bitemporal imagery. Existing end-to-end architectures are primarily designed for conventional change detection tasks that ignore unchanged objects, which limits their applicability to MCOCD. Typical MCOCD approaches are designed in a non-end-to-end manner, which leads to error accumulation and susceptibility to pseudo-changes. To address these limitations, this study presents the Feature Interaction and Fusion Network (FIF-Net), an end-to-end framework that jointly optimizes object detection and change analysis with two novel modules: the Cross-Agent Attention Module (CAAM) to effectively suppress pseudo-changes and the Sum-Difference Feature Fusion Module (SDFFM) for joint optimization of features for both changed and unchanged objects. Experimental results in the Aircraft Change Detection dataset (ACD)-v1.5 dataset demonstrate that FIF-Net achieves state-of-the-art performance of 67.6% mAP, significantly outperforming existing approaches such as Dual Correlation Attention-guided Detector (DCA-Det) and Temporal Mutual Attention and Contextual Network (TMACNet), and ablation studies further validate the efficacy of each core module.

Keywords:

Remote sensing

change detection

object detection

feature interaction

feature fusion

FIF-Net: feature interaction and fusion network for multiclass object change detection

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