Radiometric normalization (RN), also known as relative radiometric correction, is usually utilized for multi-temporal optical remote sensing image pairs. It is crucial to applications including change detection (CD) and other time-series analyses. Nevertheless, the variations across multi-temporal remote sensing image pairs are complex, containing true changes of landcover and fake changes caused by observation conditions, which poses significant difficulties for CD and other applications. For CD, the goal of RN is to well eliminate the unwanted fake changes. However, neither traditional methods nor current deep learning methods offer satisfactory solution for multi-temporal remote sensing images RN when dealing with such complicated circumstances. Towards this end, a novel pseudo invariant feature (PIF)-inspired weakly supervised generative adversarial network (GAN) for remote sensing images RN, named RS-NormGAN, is proposed to improve CD efficiently. Motivated by PIF, a sub-generator structure with different constraints is introduced to adequately deal with variant and invariant features, respectively. Besides, a global–local attention mechanism is proposed to further refine the performance by compensating spatial distortion and alleviating over-normalization and under-normalization. To verify the effectiveness of RS-NormGAN, massive experiments for CD and semantic CD across diverse scenarios have been conducted on Google Earth Bi-temporal Dataset and a constructed benchmark Sentinel-2 Hefei Change Detection Dataset. Compared with state-of-the-art methods, the proposed RS-NormGAN is very competitive, even if a simple CD network is utilized. The data and code will be available at https://gitbub.com/lixinghua5540/RS-NormGAN.

中文翻译：

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RS-NormGAN：通过有效的辐射归一化增强多时相光学遥感图像的变化检测


辐射归一化 （RN），也称为相对辐射校正，通常用于多时相光学遥感影像对。它对于变化检测 （CD） 和其他时间序列分析等应用至关重要。然而，多时相遥感影像对的变化很复杂，既有真实的土地覆盖变化，也有观测条件引起的假变化，给CD等应用带来了很大的困难。对于 CD，RN 的目标是很好地消除不需要的虚假更改。然而，在处理如此复杂的情况时，无论是传统方法还是当前的深度学习方法都没有为多时相遥感图像 RN 提供令人满意的解决方案。为此，提出了一种新的伪不变特征 （PIF） 启发的用于遥感图像 RN 的弱监督生成对抗网络 （GAN），名为 RS-NormGAN，以有效地改进 CD。在 PIF 的推动下，引入了具有不同约束条件的子生成器结构，以分别充分处理变体和不变特征。此外，提出了一种全局-局部注意力机制，通过补偿空间失真和缓解过度归一化和欠归一化来进一步细化性能。为了验证 RS-NormGAN 的有效性，在 Google Earth 双时相数据集和构建的基准 Sentinel-2 合肥变化检测数据集上对不同场景的 CD 和语义 CD 进行了大规模实验。与最先进的方法相比，即使使用简单的 CD 网络，所提出的 RS-NormGAN 也非常具有竞争力。数据和代码将在 https://gitbub.com/lixinghua5540/RS-NormGAN 上提供。