Synthetic Aperture Radar (SAR) is one of the important data sources for built-up area information acquisition and dynamic monitoring. In this paper, SARBuD1.0, a SAR image patch dataset of built-up area with GF-3 Fine strip-map mode, is introduced.The dataset is derived from 27 scenes of 10 m resolution GF-3 SAR images covering different regions in China. Approximately 60000 samples of built-up area are obtained from the SAR images. The dataset consists of built-up area SAR image patches and corresponding label images that are interpreted by experts with high-resolution optical images. The dataset contains built-up areas of different distribution types and different regions. The terrain scenes of the samples include plain area, mountain area, and plateau. The SARBuD1.0 dataset can support researchers to analyze the image features of built-up areas in different regions, assist in SAR image understanding, and provide training and test data for deep learning methods for built-up area segmentation in SAR images.In this paper, using built-up areas in mountain areas as an example, the traditional texture features and deep learning features of the built-up area are analyzed. Experiments show that a convolution neural network can provide deeper features of built-up areas compared with traditional texture features. Features in different scales can be obtained by the shallow convolutional layers of the network, and semantic information can be obtained by the deep layers. Therefore, the deep convolutional neural network classifier can detect and extract the built-up area better. Based on the dataset, three deep learning methods are applied to extract the built-up area in different terrain areas. The experimental results show that the deep learning models can achieve good results for built-up area extraction based on the dataset.The dataset can effectively support the deep learning method for big data processing. Based on the SARBuD1.0 dataset, scholars can carry out research on feature analysis and semantic segmentation of built-up areas with SAR images.

remote sensing;synthetic aperture radar (SAR);building;dataset;deep learning;GF-3;semantic segmentation