Classification with an edge: Improving semantic image segmentation with boundary detection-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_31833411/article/details/78894648

本文介绍了一种结合多个卷积神经网络(CNN)的方法来改进语义图像分割任务的表现。通过引入边界检测技术和多尺度CNN架构，提高了图像分割的准确性。具体地，使用了SEG-H编码器-解码器网络、HED-H多尺度CNN和FCN-N语义分割网络等模型。

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Classification with an edge: Improving semantic image segmentation with boundary detection

Netwroks:

SEG-H encoder-decoder network

It’s a crossbreed of FCN and encoder-decoder architecture. Use pyramid-bottleneck architecture. Compared with SEG model, SEG-H combine the DEM and data as well in the database. For channels, except coulor channela(using pascal pre-trained model), it combines DSM and nDSM channel and initialized randomly using “Xavier” weight initialization which could make the gradient magnitude roughly the same across layers. These two streams are concatenated and fed through 1x1 convolution(linearly combines the vector of feature responses at each location into a score per class). Finally those scores are further converted to probabilities with a softmax layer.
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HED-H multi-scale CNN

Add second branch for DSM. By using a regression los w.r.t. HED-H is mainly to detect the edge by height, and the color map HED-H is initialized by original HED model, and for height map it’s initialized by scratch. 这里写图片描述