2022-07-05 更新
A Strategy Optimized Pix2pix Approach for SAR-to-Optical Image Translation Task
Authors:Fujian Cheng, Yashu Kang, Chunlei Chen, Kezhao Jiang
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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MultiEarth 2022 — The Champion Solution for Image-to-Image Translation Challenge via Generation Models
Authors:Yuchuan Gou, Bo Peng, Hongchen Liu, Hang Zhou, Jui-Hsin Lai
The MultiEarth 2022 Image-to-Image Translation challenge provides a well-constrained test bed for generating the corresponding RGB Sentinel-2 imagery with the given Sentinel-1 VV & VH imagery. In this challenge, we designed various generation models and found the SPADE [1] and pix2pixHD [2] models could perform our best results. In our self-evaluation, the SPADE-2 model with L1-loss can achieve 0.02194 MAE score and 31.092 PSNR dB. In our final submission, the best model can achieve 0.02795 MAE score ranked No.1 on the leader board.
PDF CVPR 2022, MultiEarth 2022, Image-to-Image translation, competition
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