Dense Fusion Classmate Network for Land Cover Classification
This work addresses land cover classification for remote sensing applications, but it appears incremental as it builds on existing FCN-based methods without claiming major breakthroughs.
The paper tackles the challenge of precise pixel-level classification in satellite images, where large-scale elements and unclear boundaries cause missing mid-level structure information, by proposing a Dense Fusion Classmate Network (DFCNet) for land cover classification, but no concrete results or numbers are provided.
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.