Remote sensing change detection between bi-temporal images receives growing concentration from researchers. However, comparing two bi-temporal images for detecting changes is challenging, as they demonstrate different appearances. In this paper, we propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks, which regards the detection model as a generator and attains the optimal weights of the detection model without increasing the parameters of the detection model through generative-adversarial strategy, boosting the spatial contiguity of predictions. Moreover, We design a multi-level feature extractor for effectively fusing multi-level features, which adopts the pre-trained model to extract multi-level features from bi-temporal images and introduces aggregate connections to fuse them. To strengthen the identification of multi-scale objects, we propose a multi-scale adaptive fusion module to adaptively fuse multi-scale features through various receptive fields and design a context refinement module to explore contextual dependencies. Moreover, the DAGAN framework utilizes the 4-layer convolution network as a discriminator to identify whether the synthetic image is fake or real. Extensive experiments represent that the DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.

翻译：摘要：双时相遥感图像之间的变化检测日益受到研究者的关注。然而，由于双时相图像呈现不同的外观特征，通过比较两幅图像来检测变化具有挑战性。本文提出一种双注意力生成对抗网络，用于实现极高分辨率遥感图像的变化检测任务。该网络将检测模型视为生成器，通过生成对抗策略在不增加检测模型参数量的情况下获得最优权重，从而提升预测结果的空间连续性。同时，我们设计了一个多级特征提取器以有效融合多级特征，该提取器采用预训练模型从双时相图像中提取多级特征，并引入聚合连接进行特征融合。为增强多尺度目标的识别能力，我们提出多尺度自适应融合模块，通过不同感受野自适应融合多尺度特征，并设计上下文精炼模块以挖掘上下文依赖关系。此外，DAGAN框架采用4层卷积网络作为判别器，用于识别合成图像的真伪。大量实验表明，在LEVIR数据集上，DAGAN框架的平均交并比为85.01%、平均F1分数为91.48%，其性能优于现有先进方法。