ABSTRACT Extracting buildings from high spatial resolution remote sensing imagery automatically is considered as an important task in many applications. The huge differences in the appearance and spatial distribution of man-made buildings make it a challenging issue. In recent years, convolutional neural networks (CNNs) have made remarkable progress in computer vision. Many published papers have applied deep CNNs to remote sensing successfully. However, most contributions require complex structure and a big number of parameters which lead to redundant computations, and limit the application of the models. To address these issues, we propose a deep residual learning serial segmentation network called SSNet, an end-to-end semantic segmentation network, to extract buildings from high spatial resolution remote sensing imagery. SSNet reduces the network complexity and computations by drawing on the advantages of U-Net and ResNet, and improves the detection accuracy. The SSNet is extensively evaluated on two large remote sensing datasets covering a wide range of urban settlement appearances. The comparison of SSNet and state-of-the-art algorithms demonstrates the effectiveness and superiority of the proposed model for building extraction.

中文翻译：

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一种用于从遥感影像中提取建筑物的深度残差学习串行分割网络

摘要 从高空间分辨率遥感影像中自动提取建筑物被认为是许多应用中的一项重要任务。人造建筑的外观和空间分布的巨大差异使其成为一个具有挑战性的问题。近年来，卷积神经网络（CNN）在计算机视觉方面取得了显着进展。许多已发表的论文成功地将深度 CNN 应用于遥感。然而，大多数贡献需要复杂的结构和大量的参数，这导致冗余计算，并限制了模型的应用。为了解决这些问题，我们提出了一种称为 SSNet 的深度残差学习串行分割网络，这是一种端到端语义分割网络，用于从高空间分辨率遥感图像中提取建筑物。SSNet 借鉴了 U-Net 和 ResNet 的优点，降低了网络复杂度和计算量，提高了检测精度。SSNet 在两个大型遥感数据集上进行了广泛的评估，这些数据集涵盖了广泛的城市住区外观。SSNet 和最先进算法的比较证明了所提出的建筑物提取模型的有效性和优越性。