Robust semantic segmentation by dense fusion network on blurred vhr remote sensing images

说明se的设计还是很有用的

Abstract: By proposing a cascaded dense encoder-decoder network and the selayer based fusion and assembling techniques.

1. Introduction

Modified hourglasses network

Deeplab v2 and v3

实际上，遥感影像很容易被camera shakes or nosie pollution污染。

最近，一些研究人员[6]报道了使用高度信息（DSM）可以显着改善分类结果。然而，在缺陷图像的鲁棒性方面，在语义分割的背景下尚未充分研究多种模态和光谱的融合。

Lveraging dense block and designing a new hourglass convolutional network to obtain pixel-acuurate segmentation of defective images.

Contributions:

1.它设计了一种新的神经网络，用于鲁棒语义分割，利用级联密集块聚合多模态信道中的上下文信息，并使用新颖的Up块消除上采样过程中的噪声。

2.它开发了SEMix模型，以更好地融合DSM和光谱图像。

3. 引入了一系列SConv块来学习每个类别的动态权重，从而提高不平衡类别的性能。

2. Methodology

RobustDensenet :

UP block

2.1 SEMix

2.2 Sconv

Sconv与类别数目保持一致。

We use cross entropy as loss function for robustdensenet

3. Experiments and analysis

Potsdam dataset

实验结果列于表1中。随着模糊或损坏像素的面积增加（从无到50％），所提出的RobustDenseNet在整体精度和F1指标上都取得了稳定的表现。总体准确率从90.3略微下降到89.8，这可能是由于Car级（从94.7到94.0）和Imp-suf级（从92.7到91.8）的下降。相反，Deeplab v3 +，即使是语义分割的最新模型，也在稳健性测试中失败了。其平均F1得分从88.6降至86.2，总体准确率下降了2.4％。图8显示了整体精度和F1度量的变化趋势，因为像素模糊或损坏的面积增加。据观察，RobustDenseNet的降低速度比Deeplab V3 +慢。

和目前精度最高的算法比鲁棒性也没有什么问题

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