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An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images
| Content Provider | MDPI |
|---|---|
| Author | Yu, Mingyang Zhang, Wenzhuo Chen, Xiaoxian Liu, Yaohui Niu, Jingge |
| Copyright Year | 2022 |
| Description | Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder–decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale |
| Starting Page | 5151 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12105151 |
| Journal | Applied Sciences |
| Issue Number | 10 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-20 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Remote Sensing High-resolution Aerial Images Generative Adversarial Network Deep Learning Whu Building Dataset China Typical Cities Building Dataset Semantic Segmentation |
| Content Type | Text |
| Resource Type | Article |
