Gated Spatial Memory and Centroid-Aware Network for Building Instance Extraction
Gated Spatial Memory and Centroid-Aware Network for Building Instance Extraction
复制标题
用于构建实例提取的门控空间内存和质心感知网络
DOI：
10.1109/tgrs.2021.3073164
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发表时间：
2022
影响因子：
8.2
通讯作者：
Lili Guo
中科院分区：
文献类型：
--
作者：
Lele Xu;Ye Li;Jinzhong Xu;Lili Guo
高分辨率遥感影像建筑物自动提取在城市规划、摄影测量等诸多应用领域发挥着重要作用。然而，高分辨率遥感图像中复杂的背景和多样的建筑形态给建筑实例的提取带来了挑战。本文提出了一种新的两阶段实例分割网络——门控空间记忆和质心感知网络（GSMC）来解决这些问题。在我们的GSMC中开发了两个新模块，包括门控空间存储模块（GSM）和质心感知头（CH）。GSM是一个自上而下的空间结构和语义信息传输模块，其中设计了两个门，包括输入门和状态门，以加强重要的特征，补充缺乏的信息。CH是一种新的任务头，用于回归每个实例的几何中心，有助于促进对不规则形状建筑的准确和完整识别。在WHU航空数据集、WHU卫星数据集和马萨诸塞大厦数据集上的实验表明，与目前最先进的深度学习方法相比，所提出的GSMC可以获得一贯优越的性能。
Automatic building extraction from high-resolution remote sensing images plays an important role in many application fields, such as the urban planning and photogrammetry. However, the complex background and large variety in building appearances in high-resolution remote sensing images make the building instance extraction challenging. In this study, we propose a novel two-stage instance segmentation network named gated spatial memory and centroid-aware network (GSMC) to handle these problems. Two new modules, including a gated spatial memory module (GSM) and a centroid-aware head (CH), are developed in our GSMC. The GSM is a top-down spatial structure and semantic information transmission module, where two gates including an input gate and a state gate are designed to strengthen the important features and replenish the lacking information. The CH is a new task head for regressing the geometric center of each instance, which can help to promote the accurate and complete recognition for irregularly shaped buildings. Experiments on the WHU Aerial data set, the WHU Satellite data set, and the Massachusetts Building data set demonstrate that the proposed GSMC can achieve consistently superior performances when compared with the recent state-of-the-art deep learning methods.