Proceedings Article | 27 June 2023
KEYWORDS: Image segmentation, Semantics, Network architectures, Remote sensing, Deep learning, Spatial resolution, Grazing incidence, Machine learning, Forestry, Image processing
In recent years, with the development of deep learning and attention mechanism, more research has been carried out to realize semantic image segmentation based on deep learning integrated attention mechanisms. However, the current semantic segmentation methods have low segmentation accuracy, high computation cost, and serious loss of detailed information. In this paper, a lightweight designed attention gate model was introduced to reduce the computation cost. And because it can suppress irrelevant regions in the input image, while highlighting the salient features of specific tasks, the combination of the two weighting factors input features ( đť‘Ąđť‘™ ) and gating signal (g) in this structure can improve segmentation accuracy and reduce loss of detail. Therefore, this study used the weighted attention U-Net network to perform semantic segmentation on the GID dataset and finally evaluated it on the four indicators of Precision, Recall, F1-Sorce, and mIoU. This result shows that different weight values have a more significant impact on the experimental results. The attention U-Net with the best weight combination compared with the traditional U-Net network, Precision, Recall, F1-Sorce, and mIoU are increased by 0.88%, 1.4%, 1.13%, and 1.2%, respectively. Compared with the original attention UNet, Precision, Recall, F1-Sorce, and mIoU are increased by 0.86%, 1.24%, 1.04%, and 1.75%, respectively.
