Change detection (CD) from remote sensing (RS) images using deep learning has been widely investigated in the literature. It is typically regarded as a pixel-wise labeling task that aims to classify each pixel as changed or unchanged. Although per-pixel classification networks in encoder-decoder structures have shown dominance, they still suffer from imprecise boundaries and incomplete object delineation at various scenes. For high-resolution RS images, partly or totally changed objects are more worthy of attention rather than a single pixel. Therefore, we revisit the CD task from the mask prediction and classification perspective and propose MaskCD to detect changed areas by adaptively generating categorized masks from input image pairs. Specifically, it utilizes a cross-level change representation perceiver (CLCRP) to learn multiscale change-aware representations and capture spatiotemporal relations from encoded features by exploiting deformable multihead self-attention (DeformMHSA). Subsequently, a masked-attention-based detection transformers (MA-DETR) decoder is developed to accurately locate and identify changed objects based on masked attention and self-attention mechanisms. It reconstructs the desired changed objects by decoding the pixel-wise representations into learnable mask proposals and making final predictions from these candidates. Experimental results on five benchmark datasets demonstrate the proposed approach outperforms other state-of-the-art models. Codes and pretrained models are available online (https://github.com/EricYu97/MaskCD).

翻译：利用深度学习进行遥感图像变化检测（CD）已在文献中得到广泛研究。该任务通常被视为像素级标注任务，旨在将每个像素分类为变化或未变化。尽管编码器-解码器结构中的逐像素分类网络已展现出主导地位，但在各种场景下仍存在边界不精确、物体轮廓不完整的问题。对于高分辨率遥感图像，部分或完全变化的物体比单个像素更值得关注。因此，我们从掩码预测与分类的视角重新审视CD任务，提出MaskCD网络，通过自适应生成输入图像对的分类掩码来检测变化区域。具体而言，该网络利用跨层级变化表征感知器（CLCRP），通过可变形多头自注意力（DeformMHSA）机制学习多尺度变化感知表征，并从编码特征中捕获时空关系。进一步，我们开发了基于掩码注意力的检测变换器（MA-DETR）解码器，通过掩码注意力与自注意力机制精确定位并识别变化物体。该解码器将像素级表征解码为可学习的掩码提议，并从中生成最终预测，重建期望的变化物体。在五个基准数据集上的实验结果表明，所提方法优于其他最先进模型。相关代码与预训练模型已开源（https://github.com/EricYu97/MaskCD）。