融合高阶信息的遥感影像建筑物自动提取

High-order statistics integration method for automatic building extraction of remote sensing images

针对遥感影像中建筑物目标与背景环境区分度低而造成的提取效果较差的问题,本文提出了融合高阶信息的编解码网络方法以改善建筑物自动提取的精度。首先,针对遥感影像建筑提取任务,使用深度编解码网络完成对建筑物目标的低阶语义特征提取;其次,使用多项式核完成对深度网络中间特征图的高阶描述,以提升网络对于模糊特征的辨识能力;最后,将低阶特征与高阶特征级联后,送入编解码网络的末端,得到对建筑物的分割结果。在Massachusetts Buildings数据集上进行试验,其召回率、准确率和F1-score指标分别达到了85.1%,77.5%和80.9%,综合指标F1-score相比于基础深度编解码网络提升约4%。本文所提方法改进了编解码器网络对于遥感影像建筑物自动提取任务的表现性能,能够更加精确地提取与背景区分度较低的建筑物目标,具有良好的实用价值。

To address the poor performance of building extraction caused by low discrimination between the building target and background environment in remote sensing images,a high-order statistics integrated encoder-decoder network method was proposed to improve the accuracy of automatic building extraction.First,the deep encoder-decoder network was used to extract the low-order semantic features of building targets.Then,the polynomial kernels were used to achieve the high-order description of intermediate feature maps to improve the ability to recognize ambiguous features.Finally,the lower-order feature maps cascading with the higher-order features were sent to the end of the network to obtain the segmentation results of the building.Experiments on the Massachusetts Buildings dataset show that the proposed approach can achieve recall of 85.1%,precision of 77.5%and F1-score of 80.9%.Compared with the baseline network,the proposed approach is 4%higher in the metric of F1-score.The proposed method improves the performance of encoder-decoder networks for automatic building extraction of remote sensing images,and can extract building targets with low discrimination more accurately;hence,it has a good application value.