融合CNN和Transformer的遥感图像建筑物快速提取

Fast extraction of buildings from remote sensing images by fusion of CNN and Transformer

遥感图像建筑物高效提取在城市规划、灾害救援、军事侦察等领域发挥着重要作用。基于深度学习的建筑物提取方法虽然具有很高的精准度,但通常是由复杂的卷积运算和极大的网络模型实现的,提取速度低,难以满足现实需求。为此,设计了一种遥感图像建筑物快速提取方法。在STTNet模型的特征提取网络中引入多尺度卷积,在同一卷积层内提取多尺度特征,进一步提高模型的特征提取能力。改进空间稀疏特征提取器结构,在带有空间注意力权值的特征图中应用通道注意力,有效学习通道注意力权值,进而解决使用骨干网络输出特征图学习时通道注意力权值浮动的问题。为降低模型参数量,加快模型的运算速度,将STTNet模型由并联结构改为串联结构。INRIA建筑物数据集上的实验表明,本文方法在保证精度和IoU的前提下速度比STTNet提升了18.3%,明显优于主流方法。

The efficient extraction of buildings from remote sensing images plays an important role in urban planning,disaster rescue,and military reconnaissance.Building extraction methods based on deep learning have made significant progress in accuracy,especially with the sparse token transformer network(STTNet)achieving extremely high accuracy.However,these methods are usually implemented using complex convolution operations in extremely large network models,which results in low extraction speed,thereby presenting difficulties in fulfilling practical needs.Therefore,in this study,a method is designed for the fast extraction of buildings from remote sensing images.First,multi-scale convolution is introduced into the feature extraction network of the STTNet model,whereby multi-scale features are extracted in the same convolution layer to further improve the feature extraction capability of the model.Second,channel attention is applied to the feature map of the force weights,to effectively learn channel attention weights,thereby solving the problem of floating channel attention weights when using the backbone network to output the learned feature map.Finally,to reduce the number of model parameters and speed up the model,the STTNet model structure is changed from parallel to series.Experiments on the INRIA building dataset show that in terms of accuracy and the intersection over union(IoU)metric,the proposed method is 18.3%faster than STTNet and thus better than current mainstream methods.