Loading...
Please wait, while we are loading the content...
Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Li, Shaodan Fu, Shiyu Zheng, Dongbo |
| Copyright Year | 2022 |
| Description | A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas. |
| Starting Page | 1272 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su14031272 |
| Journal | Sustainability |
| Issue Number | 3 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-24 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Remote Sensing Rural Built-up Area Extraction Spectral Residual Deep Neural Network |
| Content Type | Text |
| Resource Type | Article |
