CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring

Urban areas in developing countries are experiencing rapid growth, and monitoring short-term changes has become increasingly important. For short-term monitoring, constant observation and generation of high-accuracy urban distribution maps without noise disturbance are key issues. Synthetic aperture radar (SAR) satellite images are suitable for day and night regardless of atmospheric weather condition observations for monitoring changes. We propose a method to generate high-accuracy urban distribution maps for urban change detection via SAR satellite images based using a convolutional neural network (CNN). To increase accuracy, several improvements relative to SAR polarisation combinations and dataset construction are considered in the proposed method. In addition, digital surface model (DSM) data, which are useful in the classification of land cover, were included to improve accuracy. The results demonstrate that high-accuracy urban distribution maps suitable for short-term monitoring were generated. In an evaluation, urban change data were extracted by taking the difference of urban distribution maps. A change analysis with time-series images revealed the locations of short-term urban change, and comparisons with optical satellite images validated the analysis results.