Joint content-aware and difference-transform lightweight network for remote sensing images semantic change detection

Abstract

Advancements in Earth observation technology have enabled effective monitoring of complex surface changes. Semantic change detection (SCD) using high-resolution remote sensing images is crucial for urban planning and environmental monitoring. However, existing deep learning-based SCD methods, which combine semantic segmentation (SS) and binary change detection (BCD), face challenges in lightweight design and consistency between semantic and change results, limiting their accuracy and applicability. To overcome these limitations, we propose the Joint Content-Aware and Difference-Transform Lightweight Network (CDLNet). CDLNet features a lightweight architecture, skip connections, and a multi-task decoding mechanism. The Temporal-Spatial Content-Aware Fusion module (TSAF) in the SS decoding branch incorporates change information to improve semantic classification accuracy within change regions. The Multi-Type Temporal Difference-Transform module (MTDT) in the BCD decoding branch enhances change localization for accurate SCD through efficient transformation of temporal difference features. Experiments on the SECOND, HiUCD mini, MSSCD, and Landsat-SCD datasets demonstrate that CDLNet outperforms thirteen state-of-the-art methods, achieving average improvements of 1.41%, 1.53% and 1.49% in the $F{1}_{scd}$, $IoUc$ and $Score$ metrics, respectively. Ablation studies confirm the effectiveness of the TSAF and MTDT modules and the rationality of multi-task loss weight configuration. Furthermore, CDLNet utilizes only 20% of the parameters (12.88M) and 7.5% of the FLOPs (30.11G) of the leading model, achieving an inference speed of 41 FPS, which underscores its superior lightweight characteristics. The results indicate that CDLNet offers excellent detection performance, generalization, and robustness within a lightweight framework. The code of our paper is accessible at: https://github.com/zjd1836/CDLNet.