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）图像可在不受云层覆盖及昼夜循环影响的情况下作为遥感图像源。然而，斑点噪声和图像采集条件的多样性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，以生成用于分类算法的高质量差异图像。该方法基于神经网络映射变换函数，能够根据指定采集条件从目标位置生成人工SAR图像。模型输入包括：目标位置的历史SAR图像、SAR图像的成像角度信息、数字高程模型及气象条件。采用欧洲航天局Sentinel-1 SAR图像、芬兰气象研究所的气象数据及芬兰国家土地测量局的数字高程模型，在芬兰东北部某区域进行方法验证。通过模拟SAR图像变化验证方法有效性，实验表明，相较于传统差异图生成方法，该方法在性能上取得了显著提升。