This technical report summarizes the analysis and approach on the image-to-image translation task in the Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022). In terms of strategy optimization, cloud classification is utilized to filter optical images with dense cloud coverage to aid the supervised learning alike approach. The commonly used pix2pix framework with a few optimizations is applied to build the model. A weighted combination of mean squared error and mean absolute error is incorporated in the loss function. As for evaluation, peak to signal ratio and structural similarity were both considered in our preliminary analysis. Lastly, our method achieved the second place with a final error score of 0.0412. The results indicate great potential towards SAR-to-optical translation in remote sensing tasks, specifically for the support of long-term environmental monitoring and protection.
翻译:本技术报告总结了“地球和环境挑战多模式学习”(MultiEarth 2022)中图像到图像翻译任务的分析和方法。在战略优化方面,云的分类用于过滤高云覆盖的光学图像,以协助监督的学习方法。通用的像素2pix框架,但有一些优化,用于构建模型。在损失功能中加入了平均正方差和平均绝对差的加权组合。关于评价,我们的初步分析考虑了信号比率的峰值和结构相似性。最后,我们的方法达到了第二位,最后误差得分为0.0412。结果显示,在遥感任务中,特别是在支持长期环境监测和保护方面,对合成孔对光转换有很大潜力。