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首页> 外文期刊>International journal of image and data fusion >CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring
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CNN-based generation of high-accuracy urban distribution maps utilising SAR satellite imagery for short-term change monitoring

机译：基于CNN的高精度城市分布图生成，利用SAR卫星图像进行短期变化监控

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摘要

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.
机译：发展中国家的城市地区正在快速增长，监视短期变化变得越来越重要。对于短期监控，关键问题是不断观察和生成无噪声干扰的高精度城市分布图。合成孔径雷达（SAR）卫星图像适合白天和黑夜，而无需考虑用于监视变化的大气天气情况观察。我们提出一种使用卷积神经网络（CNN）通过SAR卫星图像生成用于城市变化检测的高精度城市分布图的方法。为了提高准确性，在提出的方法中考虑了相对于SAR极化组合和数据集构造的若干改进。此外，还包括可用于土地覆被分类的数字表面模型（DSM）数据，以提高准确性。结果表明，生成了适合于短期监测的高精度城市分布图。在评估中，通过获取城市分布图的差异来提取城市变化数据。用时间序列图像进行的变化分析揭示了短期城市变化的位置，并且与光学卫星图像的比较证实了分析结果。

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