基于混合网络的异源遥感图像变化检测

为了更加准确地进行异源遥感图像的变化检测任务，提出了一种基于混合网络的异源遥感图像变化检测算法。利用伪孪生网络提取异源遥感图像块间空间维度的变化特征，利用早期融合网络提取异源遥感图像块间光谱维度的变化特征，将2支网络提取的特征进行融合，并将融合后的变化特征输入到sigmoid层进行二分类检测。同时，在伪孪生网络中加入对比损失函数，通过优化对比损失函数，使得在特征空间中，未变化图像对的空间特征差异更小，变化图像对的空间特征差异更大，以提升网络的区分能力和收敛速度。 

Heterogeneous remote sensing image change detection based on hybrid network

In order to more quickly and accurately perform the change detection task of heterogeneous remote sensing images, this paper presents a heterogeneous remote sensing image change detection algorithm based on a hybrid network. The algorithm uses a pseudo-siamese network to extract change features between the heterogeneous image blocks in spatial dimension, and uses an early fusion network to extract change features between the heterogeneous image blocks in spectral dimension. The features extracted from the two networks are fused and the fused features are input to the sigmoid layer for binary classification to determine whether the feature has changed. In addition, the contrast loss function is added to the pseudo-siamese network, so that in the features space, the spatial features of the unchanged image pair are closer, and the spatial features of the changed image pair are farther away, which is conducive to improving the network's distinction ability and convergence speed.