BT-HRSCD: High-Resolution Feature Is What You Need for a Semantic Change Detection Network With a Triple-Decoding Branch

Abstract

In recent years, semantic change detection (SCD) has emerged as a pivotal field within the remote sensing (RS) research community, underscored by its essential contribution to various Earth observation undertakings. Conventional SCD methodologies typically adopt a multitask network architecture, fusing a binary change detection (BCD) sub-task with dual semantic segmentation (SS) sub-tasks. These strategies frequently rely on the encoder’s low-resolution yet semantically dense features, derived from multiple down-sampling stages, as the inputs for the decoding heads. Departing from this traditional path and targeting the nuanced characteristics of the multisubtasks, this study pioneers a novel methodology that harnesses the potential of the encoding phase’s high-resolution features. By integrating HRNet as the encoder structure, we introduce the BT-HRSCD framework, featuring two simple and effective modules. The first, bidirectional shallow and deep features aggregation module (BiFAM), seeks to imbue features with richer semantic insights through bidirectional feature fusion that spans from shallow-to-deep as well as deep-to-shallow layers. The second module, high-resolution difference extraction (HRDE), utilizes the encoder’s highest spatial resolution features, evaluating their differences to enhance the precision in identifying change areas. BiFAM is devised to boost the SS sub-tasks’ effectiveness, whereas HRDE aims to elevate the accuracy of the BCD sub-task. Experimental results reveal that our method outperforms state-of-the-art performances relative to previous SCD efforts. Our source code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/iridescent524/BT-HRSCD</uri>.