Abstract
Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Current cross-resolution methods that are trained with samples of a fixed resolution difference (resolution ratio between the high-resolution (HR) image and the low-resolution (LR) one) may fit a certain ratio but lack adaptation to other resolution differences. Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying bitemporal resolution differences. Concretely, we synthesize blurred versions of the HR image by random downsampled reconstructions to reduce the gap between HR and LR images. We introduce coordinate-based representations to decode per-pixel predictions by feeding the coordinate query and corresponding multi-level embedding features into an MLP that implicitly learns the shape of land cover changes, therefore benefiting recognizing blurred objects in the LR image. Moreover, considering that spatial resolution mainly affects the local textures, we apply local-window self-attention to align bitemporal features during the early stages of the encoder. Extensive experiments on two synthesized and one real-world different-resolution CD datasets verify the effectiveness of the proposed method. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on the three datasets both in in-distribution and out-of-distribution settings. The empirical results suggest that our method could yield relatively consistent HR change predictions regardless of varying resolution difference ratios. Our code will be public.
Abstract (translated)
当代监督遥感(RS)图像变化检测(CD)方法大多数是针对相等分辨率的两个时间图像进行定制的。实际应用程序需要实现交叉分辨率变化检测，也就是基于具有不同空间分辨率的两个时间图像的CD方法。目前，训练样本为固定分辨率差异(高分辨率(HR)图像和低分辨率(LR)图像的分辨率比)的交叉分辨率方法可能符合一定的比率，但缺乏对其他分辨率差异的适应。为了实现持续交叉分辨率CD，我们提出按尺寸无关学习来实现模型 consistently predict HR结果，给定合成样本中不同bitemporal resolution differences的模拟样本。具体来说，我们随机剪裁重建样本以生成模糊版本的HR图像，以减少HR和LR图像之间的差异。我们引入坐标表示法，通过将坐标查询和相应的多层次嵌入特征输入到一个MLP中，使其隐含地学习地形覆盖变化的形状，因此受益于识别LR图像中的模糊物体。此外，考虑到空间分辨率主要影响 local texture，我们在编码器的早期阶段应用 local-window self-attention来对齐双时间特征。我们对两个合成和一个实际世界不同分辨率的CD数据集进行了广泛的实验，以验证所提出方法的有效性。我们的方法在分布和离分布环境下在 HR数据的分布性和离分布环境下均显著优于多个简单CD方法和两个交叉分辨率CD方法。经验结果显示，我们的方法能够产生相对一致的HR变化预测，无论分辨率差异比率如何变化。我们的代码将公开。
URL
https://arxiv.org/abs/2305.14722
