Recently, the transformer has achieved notable success in remote sensing (RS) change detection (CD). Its outstanding long-distance modeling ability can effectively recognize the change of interest (CoI). However, in order to obtain the precise pixel-level change regions, many methods directly integrate the stacked transformer blocks into the UNet-style structure, which causes the high computation costs. Besides, the existing methods generally consider bitemporal or differential features separately, which makes the utilization of ground semantic information still insufficient. In this paper, we propose the multiscale dual-space interactive perception network (MDIPNet) to fill these two gaps. On the one hand, we simplify the stacked multi-head transformer blocks into the single-layer single-head attention module and further introduce the lightweight parallel fusion module (LPFM) to perform the efficient information integration. On the other hand, based on the simplified attention mechanism, we propose the cross-space perception module (CSPM) to connect the bitemporal and differential feature spaces, which can help our model suppress the pseudo changes and mine the more abundant semantic consistency of CoI. Extensive experiment results on three challenging datasets and one urban expansion scene indicate that compared with the mainstream CD methods, our MDIPNet obtains the state-of-the-art (SOTA) performance while further controlling the computation costs.

中文翻译：

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双时态和差分特征交互感知的遥感变化检测


最近，变压器在遥感（RS）变化检测（CD）方面取得了显着的成功。其出色的远距离建模能力可以有效识别兴趣变化（CoI）。然而，为了获得精确的像素级变化区域，许多方法直接将堆叠的变压器块集成到UNet式结构中，这导致计算成本很高。此外，现有方法通常单独考虑双时态或差分特征，这使得地面语义信息的利用仍然不足。在本文中，我们提出了多尺度双空间交互式感知网络（MDIPNet）来填补这两个空白。一方面，我们将堆叠的多头变压器块简化为单层单头注意模块，并进一步引入轻量级并行融合模块（LPFM）来执行有效的信息集成。另一方面，基于简化的注意力机制，我们提出了跨空间感知模块（CSPM）来连接双时域和差分特征空间，这可以帮助我们的模型抑制伪变化并挖掘CoI更丰富的语义一致性。在三个具有挑战性的数据集和一个城市扩张场景上的大量实验结果表明，与主流CD方法相比，我们的MDIPNet在进一步控制计算成本的同时获得了最先进的（SOTA）性能。