A comparison between fully connected and deconvolutional layers for road segmentation from satellite imagery

Semantic segmentation using fully convolutional networks has quickly become a popular solution as they provide very accurate per pixel classification. However, the implementation of deconvolutional layers and their mechanics differ greatly to those of patch based segmentation using convolutional neural networks. Both techniques have been used for road segmentation from satellite imagery but never compared. Thus we investigate the difference between fully connected and deconvolutional layers and provide an interpretation as to the correlation and differences between each methodology for road segmentation from satellite imagery.