The detection of ground object changes from bi-temporal images is of great significance for urban planning, land-use/land-cover monitoring and natural disaster assessment. To solve the limitation of incomplete change detection (CD) entities and inaccurate edges caused by the loss of detailed information, this paper proposes a network based on dense connections and attention feature fusion, namely Siamese NestedUNet with Attention Feature Fusion (SNAFF). First, multi-level bi-temporal features are extracted through a Siamese network. The dense connections between the sub-nodes of the decoder are used to compensate for the missing location information as well as weakening the semantic differences between features. Then, the attention mechanism is introduced to combine global and local information to achieve feature fusion. Finally, a deep supervision strategy is used to suppress the problem of gradient vanishing and slow convergence speed. During the testing phase, the test time augmentation (TTA) strategy is adopted to further improve the CD performance. In order to verify the effectiveness of the proposed method, two datasets with different change types are used. The experimental results indicate that, compared with the comparison methods, the proposed SNAFF achieves the best quantitative results on both datasets, in which F1, IoU and OA in the LEVIR-CD dataset are 91.47%, 84.28% and 99.13%, respectively, and the values in the CDD dataset are 96.91%, 94.01% and 99.27%, respectively. In addition, the qualitative results show that SNAFF can effectively retain the global and edge information of the detected entity, thus achieving the best visual performance.

中文翻译：

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基于密集连接和注意力特征融合网络的高分辨率光学遥感图像变化检测

双时相图像地物变化检测对于城市规划、土地利用/土地覆盖监测和自然灾害评估具有重要意义。为了解决由于详细信息丢失而导致的不完整变化检测（CD）实体和不准确边缘的限制，本文提出了一种基于密集连接和注意特征融合的网络，即Siamese NestedUNet with Attention Feature Fusion（SNAFF）。首先，通过 Siamese 网络提取多级双时态特征。解码器子节点之间的密集连接用于补偿丢失的位置信息以及弱化特征之间的语义差异。然后，引入注意力机制来结合全局和局部信息，实现特征融合。最后，采用深度监督策略来抑制梯度消失和收敛速度慢的问题。在测试阶段，采用测试时间增强（TTA）策略来进一步提高CD性能。为了验证所提出方法的有效性，使用了两个具有不同变化类型的数据集。实验结果表明，与对比方法相比，所提出的SNAFF在两个数据集上都取得了最好的定量结果，其中LEVIR-CD数据集中的F1、IoU和OA分别为91.47%、84.28%和99.13%， CDD数据集中的值分别为96.91%、94.01%和99.27%。此外，定性结果表明，SNAFF能够有效保留检测到的实体的全局和边缘信息，从而实现最佳的视觉性能。