Loading...
Please wait, while we are loading the content...
Urban Water Extraction with UAV High-Resolution Remote Sensing Data Based on an Improved U-Net Model
| Content Provider | MDPI |
|---|---|
| Author | Li, Wenning Li, Yi Gong, Jianhua Feng, Quanlong Zhou, Jieping Sun, Jun Shi, Chenhui Hu, Weidong |
| Copyright Year | 2021 |
| Description | Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. |
| Starting Page | 3165 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13163165 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-10 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Deep Learning Surface Water Extraction Unmanned Aerial Vehicle (uav) Grey Level Co-occurrence Matrix Visual Features |
| Content Type | Text |
| Resource Type | Article |
