Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps. However, existing convolutional neural network and transformer-based frameworks often struggle to accurately segment semantic change regions. Moreover, transformers-based methods with standard self-attention suffer from quadratic computational complexity with respect to the image resolution, making them less practical for CD tasks with limited training data. To address these issues, we propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions while reducing the model size. Our ELGC-Net comprises a Siamese encoder, fusion modules, and a decoder. The focus of our design is the introduction of an Efficient Local-Global Context Aggregator module within the encoder, capturing enhanced global context and local spatial information through a novel pooled-transpose (PT) attention and depthwise convolution, respectively. The PT attention employs pooling operations for robust feature extraction and minimizes computational cost with transposed attention. Extensive experiments on three challenging CD datasets demonstrate that ELGC-Net outperforms existing methods. Compared to the recent transformer-based CD approach (ChangeFormer), ELGC-Net achieves a 1.4% gain in intersection over union metric on the LEVIR-CD dataset, while significantly reducing trainable parameters. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. Finally, we also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings, while achieving comparable performance. Project url https://github.com/techmn/elgcnet.

翻译：深度学习在遥感变化检测（旨在识别不同时间戳配准卫星图像对之间的语义变化区域）中已取得显著成功。然而，现有卷积神经网络与基于Transformer的框架常难以精确分割语义变化区域。此外，采用标准自注意力机制的Transformer方法因图像分辨率导致计算复杂度呈二次增长，在训练数据有限的变化检测任务中实用性欠佳。针对上述问题，我们提出高效变化检测框架ELGC-Net，通过利用丰富的上下文信息精确估计变化区域，同时缩减模型规模。ELGC-Net由孪生编码器、融合模块与解码器构成。其核心设计是在编码器中引入高效局部-全局上下文聚合模块：通过新颖的池化转置注意力和深度可分离卷积分别捕获增强的全局上下文信息与局部空间特征，其中池化转置注意力采用池化操作实现鲁棒特征提取，并借助转置注意力机制最小化计算成本。在三个具有挑战性的变化检测数据集上的大量实验表明，ELGC-Net性能优于现有方法。与近期基于Transformer的变化检测方法（ChangeFormer）相比，ELGC-Net在LEVIR-CD数据集上交并比指标提升1.4%，同时显著减少可训练参数量。所提出的ELGC-Net在遥感变化检测基准测试中树立了新的最优性能。最后，我们推出轻量化变体ELGC-Net-LW，该版本在保持可比性能的同时大幅降低计算复杂度，适用于资源受限场景。项目地址：https://github.com/techmn/elgcnet。