基于注意力密集连接金字塔网络的新增建设用地变化检测

城市新增建设用地变化迅速频繁、场景复杂等因素导致变化检测结果出现欠分割或过分割等问题,基于此本文提出了一种融合注意力机制的密集连接金字塔网络用于城市新增建设用地变化检测。在编码阶段运用卷积注意力模型提升对变化信息的关注度,突出重要特征;采用密集连接空洞卷积空间金字塔池化模块实现多尺度特征的提取与融合,提高特征的利用率与传播效率;在解码阶段通过对提取的特征图进行上采样还原图像的空间尺度特征。试验结果表明,该方法有效改善了欠分割与过分割问题,变化检测效果更好。

Detection of new construction land change based on attention intensive connection pyramid network

To settle the problems of frequent and rapid changes of new urban construction sites and complex scenarios which lead to under-segmentation or over-segmentation of change detection results,this paper proposes a densely connected pyramid network with a fused attention mechanism for urban new construction site change detection.In the coding stage,a convolutional attention model is applied to enhance the attention to change information and highlight important features;then a densely connected null convolutional spatial pyramid pooling module is used to realize the extraction and fusion of multi-scale features and improves the feature utilization and propagation efficiency;in the decoding stage,the spatial scale features of the image are restored by upsampling the extracted feature maps.The experimental results show that the method in this paper effectively improves the under-segmentation and oversegmentation problems,and the change detection effect is better.