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：基于卫星的合成孔径雷达（SAR）影像可不受云层覆盖和昼夜周期影响，作为遥感影像的数据源。然而，散斑噪声和不同的影像获取条件对变化检测分类器构成了挑战。本文提出一种改进SAR影像处理的新方法，以生成更高质量的差分图像，供分类算法使用。该方法基于神经网络映射变换函数，可根据指定获取条件下的地理位置生成人工SAR影像。模型输入包括：该地理位置的历史SAR影像、SAR影像的成像角度信息、数字高程模型以及气象条件。该方法使用欧洲空间局Sentinel-1 SAR影像、芬兰气象研究所的气象数据以及芬兰国家土地调查局的数字高程模型，以芬兰东北部某地区的数据进行测试。为验证该方法，我们模拟了SAR影像的变化，并通过实验测量其性能：与传统的差分图像生成方法相比，该方法在性能上取得了显著提升。