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A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images
1School of Information Engineering, Chang'an University, Xi'an 710064, China.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
This study introduces the E-Road model, a deep learning approach for extracting road networks from satellite imagery. E-Road achieves superior accuracy and efficiency compared to existing methods, even in complex environments.
Area of Science:
- Remote Sensing
- Computer Vision
- Deep Learning
Background:
- Accurate road network extraction from satellite imagery is crucial for various applications.
- Existing deep learning models often face challenges in balancing extraction precision and computational efficiency.
Purpose of the Study:
- To propose an efficient and accurate deep convolutional neural network model for road network extraction from satellite images.
- To improve road boundary smoothness and clarity.
Main Methods:
- Developed an encoder-decoder deep convolutional neural network model incorporating ResNet-18 and Atrous Spatial Pyramid Pooling.
- Utilized a modified cross-entropy loss function and the PointRend algorithm for training and boundary refinement.
- Employed the augmented DeepGlobe dataset and asynchronous training for model development.
Main Results:
- The proposed E-Road model demonstrates a reduced parameter count and shorter training time.
- Achieved significant performance improvements, ranging from 5.84% to 59.09%, over state-of-the-art deep models.
- The model accurately predicts road networks in complex environments.
Conclusions:
- The E-Road model offers an effective solution for precise and efficient road network extraction from satellite images.
- The model's performance indicates its potential for real-world geospatial applications.
