Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

计算机科学 数据集 卷积神经网络 人工智能 集合(抽象数据类型) 遥感 一般化 航空影像 分割 光栅图形 深度学习 卫星图像 数据挖掘 模式识别(心理学) 图像(数学) 地理 数学 数学分析 程序设计语言
作者
Shunping Ji,Shiqing Wei,Meng Lü
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:57 (1): 574-586 被引量:1218
标识
DOI:10.1109/tgrs.2018.2858817
摘要

The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and made open a high-quality multisource data set for building detection, evaluated the accuracy obtained in most recent studies on the data set, demonstrated the use of our data set, and proposed a Siamese fully convolutional network model that obtained better segmentation accuracy. The building data set that we created contains not only aerial images but also satellite images covering 1000 km 2 with both raster labels and vector maps. The accuracy of applying the same methodology to our aerial data set outperformed several other open building data sets. On the aerial data set, we gave a thorough evaluation and comparison of most recent deep learning-based methods, and proposed a Siamese U-Net with shared weights in two branches, and original images and their down-sampled counterparts as inputs, which significantly improves the segmentation accuracy, especially for large buildings. For multisource building extraction, the generalization ability is further evaluated and extended by applying a radiometric augmentation strategy to transfer pretrained models on the aerial data set to the satellite data set. The designed experiments indicate our data set is accurate and can serve multiple purposes including building instance segmentation and change detection; our result shows the Siamese U-Net outperforms current building extraction methods and could provide valuable reference.
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