关键词: 养殖池塘, 深度学习, 改进U-Net网络, 高分辨率, 精细提取

Abstract: Aiming at the problem of difficult extraction of aquaculture ponds, the deep learning method was used to extract aquaculture ponds from high spatial resolution remote sensing images. Based on the 0.5 m high-resolution remote sensing satellite images, this study adopted DenseNet network structure as the encoder of the U-Net network model, used the hand-marked aquaculture pond training samples to train the improved U-Net network model, and used the network model after training to extract the aquaculture ponds in the validation set images. Resulte showed that the precision rate, recall rate, and intersection over union of the improved U-Net network model were 92.77%, 92.21% and 85.60% respectively. Compared with the object-oriented method and the D-LinkNet model method, the improved U-Net network model possessed the best result. This model provided a new idea and method for fine extraction of aquaculture ponds, which was beneficial to promote the investigation and fine management of aquaculture resources.

Key words: aquaculture ponds, deep learning, the improved U-Net network, high-resolution remote sensing, fine extraction