Fig. 1 | Scientific Reports

Fig. 1

From: Spatially adaptive interaction network for semantic segmentation of high-resolution remote sensing images

Fig. 1

Comparison of SAINet with other networks in semantic segmentation of high-resolution remote sensing images: (a) Single-branch networks, typically train on downsampled images, resulting in insufficient capability to extract local detailed information. (b) Dual-branch networks, train with both global and local branches, fuse all the information from different branches, and treat each feature equally. (c) Our proposed SAINet, dynamic interaction of information across different features. The images used in this figure are sourced from the DeepGlobe dataset, and the dataset can be accessed at: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset.

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