Abstract

Building objects is one of the principal features that are essential for updating the geospatial database. Extracting building features from high-resolution imagery automatically and accurately is challenging because of the existence of some obstacles in these images, such as shadows, trees, and cars. Although deep learning approaches have shown significant improvements in the results of image segmentation in recent years, most deep neural networks still cannot achieve highly accurate results with correct segmentation map when processing high-resolution remote sensing images. Therefore, we implemented a new deep neural network named Seg–Unet method, which is a composition of Segnet and Unet techniques, to exploit building objects from high-resolution aerial imagery. Results obtained 92.73% accuracy carried on the Massachusetts building dataset. The proposed technique improved the performance to 0.44%, 1.17%, and 0.14% compared with fully convolutional neural network (FCN), Segnet, and Unet methods, respectively. Results also confirmed the superiority of the proposed method in building extraction.

摘要

构建对象是更新地理空间数据库必不可少的主要功能之一。从高分辨率图像中自动准确地提取建筑物特征具有挑战性，因为这些图像中存在一些障碍物，例如阴影、树木和汽车。尽管近年来深度学习方法在图像分割的结果上取得了显着的进步，但在处理高分辨率遥感图像时，大多数深度神经网络仍然无法通过正确的分割图获得高度准确的结果。因此，我们实施了一种名为 Seg-Unet 方法的新深度神经网络，它是 Segnet 和 Unet 技术的组合，以利用高分辨率航空图像中的建筑对象。结果在马萨诸塞州建筑数据集上获得了 92.73% 的准确率。与完全卷积神经网络 (FCN)、Segnet 和 Unet 方法相比，所提出的技术将性能分别提高了 0.44%、1.17% 和 0.14%。结果也证实了该方法在建筑物提取中的优越性。