基于改进U-Net模型的高分辨率遥感影像中城市建筑物的提取

Extraction of urban buildings from high-resolution remote sensing images based on improved U-Net model

针对高分辨率遥感影像中城市建筑物周围环境复杂多样,易被阴影遮挡,难以精细化提取的问题,提出一种改进的U-Net网络用于图像中的城市建筑物提取.该网络在标准U-Net网络的编码器末端嵌入双重注意力模块,可以通过捕获全局建筑物信息和长通道建筑物信息,实现建筑物特征的增强.在交叉熵损失函数的基础上加入Lovász损失函数,构成的复合损失函数增强了对建筑物提取结果的约束能力,进一步提高了模型的鲁棒性.将该模型在美国马萨诸塞州数据集上进行验证,提取建筑物的F1-score为87.83%.结果表明,本方法对高分辨率遥感影像中周围环境复杂多样、被阴影遮挡的城市建筑物具有较强的提取能力.

Urban buildings in high resolution remote sensing images usually have the characteristics of intricate surroundings and shadows,which make fine-scale image information extraction a problematic task.An improved U-Net network was proposed for extraction of urban buildings from high-resolution remote sensing images.The network embedded a dual attention module at the end of the encoder of the standard U-Net network.This module could capture global and long-channel building information to enhance building features.The Lovász loss function was added on the basis of the cross-entropy loss function,and the composite loss function formed enhanced the constraint ability of the building extraction results,and further improved the robustness of the model.Our results showed the F1-score was 87.83% verified by the public Massachusetts building dataset.The findings suggested that the improved U-Net could be a robust solution to extracting high-resolution urban buildings.