基于监督对比学习正则化的高分辨率SAR图像建筑物提取方法

Supervised Contrastive Learning Regularized High-resolution Synthetic Aperture Radar Building Footprint Generation

近年来,高分辨合成孔径雷达(SAR)图像的智能解译技术在城市规划、变化监测等方面得到了广泛应用。不同于光学图像,SAR图像的获取方式、图像中目标的几何结构等因素制约了现有深度学习方法对SAR图像地物目标的解译效果。该文针对高分辨SAR图像城市区域建筑物提取,提出了基于监督对比学习的正则化方法,其主要思想是增强同一类别像素在特征空间中的相似性以及不同类别像素之间的差异性,使得深度学习模型能更加关注SAR图像中建筑物与非建筑物区域在特征空间中的区别,从而提升建筑物识别精度。利用公开的大场景SpaceNet6数据集,通过对比实验,提出的正则化方法,其建筑物提取精度相比于常用的分割方法在不同网络结构下至少提升1%,分割结果证明了该文方法在实际数据上的有效性,可以对复杂场景下的城市建筑物区域进行有效分割。此外,该方法也可以拓展应用于其他SAR图像像素级别的地物分割任务中。

Over the recent years,high-resolution Synthetic-Aperture Radar(SAR)images have been widely applied for intelligent interpretation of urban mapping,change detection,etc.Different from optical images,the acquisition approach and object geometry of SAR images have limited the interpretation performances of the existing deep-learning methods.This paper proposes a novel building footprint generation method for highresolution SAR images.This method is based on supervised contrastive learning regularization,which aims to increase the similarities between intra-class pixels and diversities of interclass pixels.This increase will make the deep learning models focus on distinguishing building and nonbuilding pixels in latent space,and improve the classification accuracy.Based on public SpaceNet6 data,the proposed method can improve the segmentation performance by 1%compared to the other state-of-the-art methods.This improvement validates the effectiveness of the proposed method on real data.This method can be used for building segmentation in urban areas with complex scene background.Moreover,the proposed method can be extended for other types of landcover segmentation using SAR images.