ResU-Former: Advancing Remote Sensing Image Segmentation with Swin Residual Transformer for Precise Global–Local Feature Recognition and Visual–Semantic Space Learning

Computer Science

• Class Distribution 33%
• Complex Background 33%
• Complex Environment 33%
• Contextual Information 33%
• Embedded Information 33%
• Experimental Result 33%
• Feature Recognition 100%
• Higher Semantic Level 33%
• Image Segmentation 100%
• Learning Space 100%
• Recognition Accuracy 33%
• Remote Sensing Image 100%
• Segmentation Task 33%

Engineering

• Fundamental Unit 50%