论文标题

策略优化SAR到光学图像翻译任务的PIX2PIX方法

A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task

论文作者

Cheng, Fujian, Kang, Yashu, Chen, Chunlei, Jiang, Kezhao

论文摘要

该技术报告总结了地球和环境挑战多模式学习中图像到图像翻译任务的分析和方法(Multiiearth 2022)。在策略优化方面,云分类用于过滤具有密集云覆盖的光学图像,以帮助有监督的学习方法。使用一些优化的常用Pix2Pix框架来构建模型。均方根误差和平均绝对误差的加权组合被纳入损耗函数。至于评估,在我们的初步分析中,都考虑了峰值与信号比和结构相似性。最后,我们的方法以0.0412的最终错误得分获得了第二名。结果表明,在遥感任务中,特别是为了支持长期的环境监测和保护,有很大的潜力朝着光学转换。

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.

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