| 刊名 | Meteorological and Environmental Research |
| 作者 | Yu PEI, Xi SHEN, Xianwu YANG, Kaiyu FU, Qinfang ZHOU* |
| 作者单位 | Yunnan Map Institute, Kunming 650034, China |
| DOI | 10.19547/j.issn2152-3940.2025.01.016 |
| 年份 | 2025 |
| 刊期 | 1 |
| 页码 | 64-69,75 |
| 关键词 | Deep learning; Fully convolutional network; Semantic segmentation; Law enforcement of land satellite images; Extraction of suspected illegal buildings |
| 摘要 | In the management of land resources and the protection of cultivated land, the law enforcement of land satellite images is often used as one of the main means. In recent years, the policies and regulations of the law enforcement of land satellite images have become more and more strict and adjusted increasingly frequently, which plays a decisive role in preventing excessive non-agricultural and non-food urbanization. In the process of the law enforcement, the extraction of suspected illegal buildings is the most important and time-consuming content. Compared with the traditional deep learning model, fully convolutional networks (FCN) has a great advantage in remote sensing image processing because its input images are not limited by size, and both convolution and deconvolution are independent of the overall size of images. In this paper, an intelligent extraction model of suspected illegal buildings in land satellite images based on deep learning FCN was built. Kaiyuan City, Yunnan Province was taken as an example. The verification results show that the global accuracy of this model was 86.6% in the process of building extraction, and mean intersection over union (mIoU) was 73.6%. This study can provide reference for the extraction of suspected illegal buildings in the law enforcement work of land satellite images, and reduce the tedious manual operation to a certain extent. |
