Canny Enhanced High-Resolution Neural Network for Satellite Image Based Land Cover Classification and Its Application in Wireless Channel Simulations

Satellite image based land cover classification, which falls under the category of semantic segmentation, is critical for many global and environmental applications. Deep learning has been proven to be excellent in semantic segmentation. However, mainstream neural networks formed by connecting high-to-low convolutions in series are prone to losing image information, which affects the accuracy of semantic segmentation. Besides, it is difficult to distinguish adjacent land cover classes with similar colors using only RGB information presented by satellite images. Striven to maintain high-resolution representations and improve the inter-class distinguishability, a Canny enhanced high-resolution neural network (C-HRNet) is proposed based on Canny edge features and the high-resolution neural network (HRNet) that maintains high-resolution representations throughout the process. Meanwhile, we construct a novel dataset for model evaluation, which provides an automatic dataset construction method, making the dataset more efficient in construction and richer in samples. Extensive experiments are conducted on datasets at different granularities. Quantitative results demonstrate that for large-scale fine-grained scenarios, C-HRNet outperforms state-of-the-art semantic segmentation networks due to the accurate spatial localization ability of Canny edge features. For small-scale coarse-grained scenarios, Canny extracts a large number of edge features that highlight the positional differences between adjacent instances belonging to the same land cover class, which slightly degrades the performance of C-HRNet, but it can still provide reliable land cover classification results. Based on this conclusion, we apply C-HRNet to large-scale wireless channel simulations that are location-sensitive and require fine-grained semantic segmentation, which are proven to be accurate and effective.

Automated summary

Satellite image based land cover classification is critical for global and environmental applications, and deep learning has been proven to be excellent in semantic segmentation. However, traditional neural networks easily lose image information and it is difficult to distinguish adjacent land cover classes with similar colors. In order to improve inter-class distinguishability, a Canny enhanced high-resolution neural network (C-HRNet) is proposed based on Canny edge features and the high-resolution neural network (HRNet). Experimental results show that C-HRNet outperforms state-of-the-art semantic segmentation networks in large-scale fine-grained scenarios.