Remote Sensing, Volume 16, Issue 15
2024 August-1 - 183 articles
Cover Story: Space-based atmospheric lidars provide critical information about the vertical distribution of clouds and aerosols. This is useful for studying Earth's radiation budget and climate. Detection of hazardous volcanic or smoke plumes is another important application. However, the photon counting detectors which give these lidars excellent sensitivity at night are strongly affected by solar background during the day. Horizontally averaging the data to 33 or more times the original distance is required for daytime feature detection to even approach the level achievable at nighttime. We trained a state-of-the-art deep learning image denoising model using raw CATS signals. Tests on simulated and real data showed the resulting denoised signal allowed more accurate feature detection at much higher resolution compared with standard averaging. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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