Hao Chang;Peijin Wang;Wenhui Diao;Guangluan Xu;Xian Sun
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引用次数: 0
摘要
最近,变压器在遥感(RS)变化探测(CD)方面取得了显著的成功。其出色的远距离建模能力可以有效识别感兴趣的变化(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换器块集成到 UNet 样式的结构中,导致计算成本较高。此外,现有方法一般都是单独考虑位时或差分特征,对地面语义信息的利用还不够充分。本文提出了多尺度双空间交互感知网络(MDIPNet)来填补这两个空白。一方面,我们将堆叠的多头变换模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),以实现高效的信息整合。另一方面,在简化注意力机制的基础上,我们提出了跨空间感知模块(CSPM)来连接位时特征空间和差分特征空间,这可以帮助我们的模型抑制伪变化,挖掘出更丰富的CoI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,与主流的 CD 方法相比,我们的 MDIPNet 在进一步控制计算成本的同时,获得了最先进的性能(SOTA)。
Remote Sensing Change Detection With Bitemporal and Differential Feature Interactive Perception
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.