Urban Water Extraction with UAV High-Resolution Remote Sensing Data Based on an Improved U-Net Model
Wenning Li, Yi Li, Jianhua Gong, Quanlong Feng, Jieping Zhou, Jun Sun, Chenhui Shi, Weidong Hu
Abstract
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.