The up-to-date building information is significant to urban planning and economic assessment. Automatic building change detection (BCD) from bi-temporal remote sensing images is essential for updating building status efficiently. Nevertheless, BCD remains challenging due to the complex building appearance, the diverse imaging conditions, and the building’s positional inconsistencies between the bi-temporal images. Recent convolutional neural network-based BCD methods have achieved impressive performance. However, most existing methods employed subtraction or concatenation to identify building changes. Such simple change-deciding operations ignore the spatial–temporal correlation between the bi-temporal features and cannot model the building changes effectively, resulting in overmuch misclassifications. This paper proposes a hierarchical XOR approximating network CDasXORNet to model building changes robustly. An XOR approximation operation is proposed to produce discriminative building differential features from the bi-temporal inputs. We assume that BCD and the logical XOR function have the same nature (i.e., when the two inputs are identical, the output is unchanged/False; otherwise, it is changed/True). This applies to the building change and unaltered pixels simultaneously. Thus, by approximating XOR operation, CDasXORNet can simultaneously exploit the spatial–temporal correlation and the changed and changeless information of buildings. Hierarchical XOR approximation operations are subsequently designed, which process only high-level features to mitigate the influence of substantial irrelevant spectral differences. In addition, the residual linear attention mechanism is introduced to refine the building change features further. Experiments on three publicly challenging datasets demonstrate that our method achieves promising BCD results with fewer commission errors and higher overall performance than the comparative approaches.

中文翻译：

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CDasXORNet：将双时态遥感图像中的建筑物变化检测作为异或问题

最新的建筑信息对于城市规划和经济评估具有重要意义。双时态遥感图像的自动建筑物变化检测（BCD）对于有效更新建筑物状态至关重要。然而，由于建筑物外观复杂、成像条件多样以及双时态图像之间建筑物的位置不一致，BCD 仍然具有挑战性。最近基于卷积神经网络的 BCD 方法取得了令人印象深刻的性能。然而，大多数现有方法采用减法或串联来识别建筑物的变化。这种简单的变化决策操作忽略了双时态特征之间的时空相关性，无法有效地对建筑物变化进行建模，从而导致过多的错误分类。本文提出了一种分层异或近似网络 CDasXORNet 来稳健地建模构建变化。提出了异或近似运算，以从双时态输入中产生有区别的建筑差异特征。我们假设BCD和逻辑异或函数具有相同的性质（即当两个输入相同时，输出不变/False；否则，改变/True）。这同时适用于建筑物变化和未改变的像素。因此，通过近似XOR运算，CDasXORNet可以同时利用时空相关性以及建筑物的变化和不变信息。随后设计了分层异或近似运算，该运算仅处理高级特征以减轻实质上不相关的光谱差异的影响。此外，引入残差线性注意机制进一步细化建筑物变化特征。对三个公开具有挑战性的数据集进行的实验表明，与比较方法相比，我们的方法取得了有希望的 BCD 结果，并且佣金错误更少，总体性能更高。