Research Article
Deep Neural Network Based on Sparse Auto-Encoder for Road Extraction
@INPROCEEDINGS{10.1007/978-3-031-65126-7_12, author={Sheng Liu and Shuxiao Chang and Ting Cao and Xinyue Li}, title={Deep Neural Network Based on Sparse Auto-Encoder for Road Extraction}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part I}, proceedings_a={QSHINE}, year={2024}, month={8}, keywords={Road extraction aerial image Deep learning Convolutional Neural Network Sparse Auto-encoder}, doi={10.1007/978-3-031-65126-7_12} }- Sheng Liu
Shuxiao Chang
Ting Cao
Xinyue Li
Year: 2024
Deep Neural Network Based on Sparse Auto-Encoder for Road Extraction
QSHINE
Springer
DOI: 10.1007/978-3-031-65126-7_12
Abstract
Road extraction from aerial image has realistic significance for GIS data updating. In view of the complexity challenging for acquiring road information, this paper proposes supervised model that combines Convolutional Neural Network (CNN) with Sparse Auto-Encoder (SAE) to cope with the road extraction task. First, the road features are extracted from the amount of non-annotated data using SAE model that aim to train the road features using CNN principle with implementing convolution and pooling to reduce model complexity. Second, the encoder network completes the operation, and after the deep pooling and deconvolution operations, the intermediate features are extracted by the decoder network and sampled back to the input image of the same size on the map. Third, the soft-max classifier categorizes images into roads and non-roads. Finally, the experiments verify that the proposed method outperforms the traditional methods and could achieve the satisfy result.
