Road extraction from aerial images has been a hot research topic in the field\nof remote sensing image analysis. In this letter, a semantic segmentation\nneural network which combines the strengths of residual learning and U-Net is\nproposed for road area extraction. The network is built with residual units and\nhas similar architecture to that of U-Net. The benefits of this model is\ntwo-fold: first, residual units ease training of deep networks. Second, the\nrich skip connections within the network could facilitate information\npropagation, allowing us to design networks with fewer parameters however\nbetter performance. We test our network on a public road dataset and compare it\nwith U-Net and other two state of the art deep learning based road extraction\nmethods. The proposed approach outperforms all the comparing methods, which\ndemonstrates its superiority over recently developed state of the arts.\n
This paper presents GridNet, a new Convolutional Neural Network (CNN)\narchitecture for semantic image segmentation (full scene labelling). Classical\nneural networks are implem...
U-net is an image segmentation technique developed primarily for medical\nimage analysis that can precisely segment images using a scarce amount of\ntraining data. These traits ...
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an Ima...
The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and m...
Single image super-resolution (SISR) is a notoriously challenging ill-posed\nproblem, which aims to obtain a high-resolution (HR) output from one of its\nlow-resolution (LR) ver...
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