Generating high-accuracy urban distribution map for short-term change monitoring based on convolutional neural network by utilizing SAR imagery

In the developing countries, urban areas are expanding rapidly. With the rapid developments, a short term monitoring of urban changes is important. A constant observation and creation of urban distribution map of high accuracy and without noise pollution are the key issues for the short term monitoring. SAR satellites are highly suitable for day or night and regardless of atmospheric weather condition observations for this type of study. The current study highlights the methodology of generating high-accuracy urban distribution maps derived from the SAR satellite imagery based on Convolutional Neural Network (CNN), which showed the outstanding results for image classification. Several improvements on SAR polarization combinations and dataset construction were performed for increasing the accuracy. As an additional data, Digital Surface Model (DSM), which are useful to classify land cover, were added to improve the accuracy. From the obtained result, high-accuracy urban distribution map satisfying the quality for short-term monitoring was generated. For the evaluation, urban changes were extracted by taking the difference of urban distribution maps. The change analysis with time series of imageries revealed the locations of urban change areas for short-term. Comparisons with optical satellites were performed for validating the results. Finally, analysis of the urban changes combining X-band, L-band and C-band SAR satellites was attempted to increase the opportunity of acquiring satellite imageries. Further analysis will be conducted as future work of the present study.