基于HRNet的高分辨率遥感影像建筑物变化信息提取

建筑物图斑变化检测是遥感影像信息提取的重要内容之一,对于土地调查、自然资源常态化监测、土地执法监测等具有重要意义。岭南地区建设结构复杂,高分辨率遥感影像信息丰富,包含建筑结构细节多种多样,加上成像的季节不同、时间不同等因素导致建筑物变化信息的自动提取十分困难。针对此问题,本文提出了基于HRNet的语义分割模型,通过筛选保留高分辨率的特征层,从而保留更细节的图像信息。此外,结合图像分割二值化对结果进行优化,在一定程度上提高了高分辨率遥感影像建筑物变化自动检测的能力。

HRNet-based extraction of building change information from high-resolution remote sensing images

Building change extraction is one of the important research areas of remote sensing image information extraction, which is of great significance for land survey, natural resources monitoring and land law enforcement. The complex construction structure in Lingnan area of China contains a variety of building structure details, which reflect rich information on the high-resolution remote sensing images, and the factors of influence such as abundant data sources and imaging seasonal differences. It makes the automatic extraction of building change information very difficult. To address this problem, this paper proposes a semantic segmentation model based on HRNet, which achieves the retention of more detailed texture information by screening the feature layers that retain high resolution. On this basis, the automatic detection capability of building changes in high-resolution remote sensing images is improved by GraphCut binarization optimization.