Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images.

翻译：合成孔径雷达（SAR）图像可以作为一种遥感影像的来源，不受云层和昼夜变换的影响。然而，斑点噪声和不同的成像条件给变化检测分类器带来了挑战。本文提出了一种改进SAR图像处理的新方法，以产生更高质量的差分图像，用于分类算法。该方法基于基于神经网络的映射转换函数构建，该转换函数从请求的成像条件中的位置产生人工SAR图像。模型的输入为：来自该位置的先前SAR图像、SAR图像的成像角信息、数字高程模型和天气条件。该方法通过使用来自欧洲空间局的Sentinel-1 SAR图像，来自芬兰气象研究所的天气数据和来自芬兰国家测量署的数字高程模型的数据，在芬兰东北部的一个位置进行了测试。为了验证该方法，对SAR图像进行了模拟变化，并使用实验来衡量所提出的方法的性能，结果表明与更常规的差分图像创建方法相比，该方法的性能得到显著提高。