Dense Fusion Classmate Network for Land Cover Classification
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Abstract (arXiv)
Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always extend to large scope and no regular or clear boundaries. Therefore, effective mid-level structure information extremely missing, precise pixel-level classification becomes tough issues. In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover classification.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1.
3 papers.
- 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 261
- 2021Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagerycited by 18
- 2023Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensingcited by 0
- 2023Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing29 Jan 2023
- 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation17 Oct 2021
- 2021Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery1 Apr 2021