IMPROVEMENT OF CNN-BASED ROAD EXTRACTION FROM SATELLITE IMAGES VIA MORPHOLOGICAL IMAGE PROCESSING

In this paper, we propose to improve the recall of CNN-based road extraction from satellite images by means of label thickening and thinning With the thickened road labels, the CNN is led to extract roads more aggressively, preserving the topological information of roads. After inference, the predicted segment maps need to be thinned back to the original width. The proposed technique has been evaluated with an existing road extraction dataset in various degrees of thickening. Throughout the experiments, the relaxed recall score has been successfully improved by the proposed technique, reducing the number of false-negative pixels. However, at the same time, it has been observed that the number of false-positive pixels also increases slightly. Overall, it has been visually observed that the topological information of roads such as connectivity is better extracted by the proposed technique.