Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 5, Pages 709-713Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2672734
Keywords
Convolutional neural network (CNN); machine learning; road extraction
Categories
Funding
- National Nature Science Foundation of China [61573037, 61471022]
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In this letter, we propose a road structure refined convolutional neural network (RSRCNN) approach for road extraction in aerial images. In order to obtain structured output of road extraction, both deconvolutional and fusion layers are designed in the architecture of RSRCNN. For training RSRCNN, a new loss function is proposed to incorporate the geometric information of road structure in cross-entropy loss, thus called road-structure-based loss function. Experimental results demonstrate that the trained RSRCNN model is able to advance the state-of-the-art road extraction for aerial images, in terms of precision, recall, F-score, and accuracy.
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