Combined Improved Dirichlet Models and Deep Learning Models for Road Extraction from Remote Sensing Images

• Published in: Canadian journal of remote sensing Vol. 47; no. 3; pp. 465 - 484
• Main Authors: Chen, Ziyi, Wang, Cheng, Li, Jonathan, Zhong, Bineng, Du, Jixiang, Fan, Wentao
• Format: Journal Article
• Language: English
• Published: Cogent 04.05.2021; Taylor & Francis Group
• ISSN: 0703-8992; 1712-7971
• DOI: 10.1080/07038992.2021.1937087

Combining Dirichlet Mixture Models (DMM) with deep learning models for road extraction is an attractive study topic. Benefiting from DMM, the manually labeling work is alleviated. However, DMM suffers from high computational complexity due to pixel by pixel computations. Also, traditional constant parameter settings of DMM may not be suitable for different target images. To address the above problems, we propose an improved DMM which embeds superpixel strategy and sparse representation into DMM. In our road extraction framework, we first use improved DMM to filter out most backgrounds. Then, a trained deep CNN model is used for further precise road area recognition. To further promote the processing speed, we also apply the superpixel scanning strategy for CNN models. We tested our method on a Shaoshan dataset and proved that our method not only can achieve better results than other compared state-of-the-art image segmentation methods, but the processing speed and accuracy of DMM are also improved.