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Gated Spatial Memory and Centroid-Aware Network for Building Instance Extraction
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DOI：10.1109/TGRS.2021.3073164.png)
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摘要
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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.
Keyword:
Feature extraction
Buildings
Logic gates
Image segmentation
Semantics
Task analysis
Shape
Building extraction
centroid-aware head (CH)
gating mechanism
instance segmentation
remote sensing image
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8.6
论文数:
2.1W
被引数:
10.7W
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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network使用门控残差细化网络从高分辨率航空图像和LiDAR数据中自动提取建筑物
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