Remote sensing (RS) change detection methods can extract critical information on surface dynamics and are an essential means for humans to understand changes in the earth's surface and environment. Among these methods, semantic change detection (SCD) can more effectively interpret the multi-class information contained in bi-temporal RS imagery, providing semantic-level predictions that support dynamic change monitoring. However, due to the limited semantic understanding capability of the model and the inherent complexity of the SCD tasks, existing SCD methods face significant challenges in both performance and paradigm complexity. In this paper, we propose PerASCD, a SCD method driven by RS foundation model PerA, designed to enhance the multi-scale semantic understanding and overall performance. We introduce a modular Cascaded Gated Decoder (CG-Decoder) that simplifies complex SCD decoding pipelines while promoting effective multi-level feature interaction and fusion. In addition, we propose a Soft Semantic Consistency Loss (SSCLoss) to mitigate the numerical instability commonly encountered during SCD training. We further explore the applicability of multiple existing RS foundation models on the SCD task when equipped with the proposed decoder. Experimental results demonstrate that our decoder not only effectively simplifies the paradigm of SCD, but also achieves seamless adaptation across various vision encoders. Our method achieves state-of-the-art (SOTA) performance on two public benchmark datasets, validating its effectiveness. The code is available at https://github.com/SathShen/PerASCD.git.

翻译：遥感变化检测方法能够提取地表动态的关键信息，是人类理解地表与环境变化的重要手段。其中，语义变化检测能够更有效地解读双时相遥感图像中包含的多类别信息，提供支持动态变化监测的语义级预测。然而，由于模型语义理解能力的局限以及语义变化检测任务固有的复杂性，现有方法在性能与范式复杂度方面均面临显著挑战。本文提出PerASCD，一种由遥感基础模型PerA驱动的语义变化检测方法，旨在增强多尺度语义理解与整体性能。我们引入了一种模块化的级联门控解码器，该解码器在简化复杂语义变化检测解码流程的同时，促进了有效的多层次特征交互与融合。此外，我们提出了软语义一致性损失函数，以缓解语义变化检测训练中常见的数值不稳定问题。我们进一步探究了多种现有遥感基础模型在配备所提解码器时对语义变化检测任务的适用性。实验结果表明，我们的解码器不仅能有效简化语义变化检测的范式，还能实现跨多种视觉编码器的无缝适配。我们的方法在两个公开基准数据集上取得了最先进的性能，验证了其有效性。代码发布于 https://github.com/SathShen/PerASCD.git。