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.

提出一种密集融合卷积神经网络 (DFCNet)，用于解决卫星图像中缺少有效中层结构信息造成像素级分类困难的问题，主要用于土地覆盖分类。

用于土地覆盖分类的密集融合同学网络

Dense Fusion Classmate Network for Land Cover Classification

We present the first fully convolutional end-to-end solution for
instance-aware semantic segmentation task. It inherits all the merits of FCNs
for semantic segmentation and instance mask proposal. It performs instance mask
prediction and classification jointly. The underlying convolutional
representation is fully shared between the two sub-tasks, as well as between
all regions of interest. The proposed network is highly integrated and achieves
state-of-the-art performance in both accuracy and efficiency. It wins the COCO
2016 segmentation competition by a large margin. Code would be released at
https://github.com/daijifeng001/TA-FCN.

本文提出了第一个完全基于卷积的端到端解决方案，用于实例感知的语义分割任务，它继承了 FCNs 的所有优点，包括用于语义分割和实例掩码提议的实例掩码预测和分类，并且在这两个子任务以及 ROI 的所有区域之间完全共享其底层卷积表示，所建议的网络高度集成，并在准确性和效率方面均实现了最先进的性能。它以很大的优势赢得了 COCO 2016 分割竞赛。