Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network

TL;DR

This paper introduces an adaptive deep belief network approach for automatic road network extraction from satellite images, improving accuracy and inference speed over traditional CNN methods.

Contribution

The study develops an adaptive structural learning method for DBN applied to road network recognition, replacing CNN to enhance performance.

Findings

Adaptive DBN outperforms CNN in detection accuracy.

Adaptive DBN reduces inference time.

Model effectively extracts road networks from satellite images.

Abstract

In our research, an adaptive structural learning method of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) has been developed as one of prominent deep learning models. The neuron generation-annihilation in RBM and layer generation algorithms in DBN make an optimal network structure for given input during the learning. In this paper, our model is applied to an automatic recognition method of road network system, called RoadTracer. RoadTracer can generate a road map on the ground surface from aerial photograph data. In the iterative search algorithm, a CNN is trained to find network graph connectivities between roads with high detection capability. However, the system takes a long calculation time for not only the training phase but also the inference phase, then it may not realize high accuracy. In order to improve the accuracy and the calculation time, our Adaptive DBN was implemented on the RoadTracer instead of the CNN. The performance of our developed model was evaluated on a satellite image in the suburban area, Japan. Our Adaptive DBN had an advantage of not only the detection accuracy but also the inference time compared with the conventional CNN in the experiment results.