A Multi-Task Consistency Enhancement Network for Semantic Change Detection in HR Remote Sensing Images and Application of Non-Agriculturalization

Abstract

It is challenging to investigate semantic change detection (SCD) in bi-temporal high-resolution (HR) remote sensing images. For the non-changing surfaces in the same location of bi-temporal images, existing SCD methods often obtain the results with frequent errors or incomplete change detection due to insufficient performance on overcoming the phenomenon of intraclass differences. To address the above-mentioned issues, we propose a novel multi-task consistency enhancement network (MCENet) for SCD. Specifically, a multi-task learning-based network is constructed by combining CNN and Transformer as the backbone. Moreover, a multi-task consistency enhancement module (MCEM) is introduced, and cross-task mapping connections are selected as auxiliary designs in the network to enhance the learning of semantic consistency in non-changing regions and the integrity of change features. Furthermore, we establish a novel joint loss function to alleviate the negative effect of class imbalances in quantity during network training optimization. We performed experiments on publicly available SCD datasets, including the SECOND and HRSCD datasets. MCENet achieved promising results, with a 22.06% Sek and a 37.41% Score on the SECOND dataset and a 14.87% Sek and a 30.61% Score on the HRSCD dataset. Moreover, we evaluated the applicability of MCENet on the NAFZ dataset that was employed for cropland change detection and non-agricultural identification, with a 21.67% Sek and a 37.28% Score. The relevant comparative and ablation experiments suggested that MCENet possesses superior performance and effectiveness in network design.

Keywords

• high-resolution images
• semantic change detection
• multi-task learning
• non-agriculturalization