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

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Abstract
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.
Funding Information
  • Natural Science Foundation of Fujian Province (2019J01081)
  • National Natural Science Foundation of China
  • United National Natural Science Foundation of China (U1605254)
  • National Natural Science Foundation of China (6187606, 61972167 and 61673186)
  • project of science and technology plan of Fujian Province

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