Abstract

Lidar is a powerful active remote sensing device used in the detection of the optical properties of aerosols and clouds. However, there are difficulties in layer detection and classification. Many previous methods are too complex for large dataset analysis or limited to data with too high a signal-to-noise ratio (SNR). In this study, a mechanism of multiscale detection and overdetection rejection is proposed based on a trend index function that we define. Finally, we classify layers based on connected layers employing a quantity known as the threshold of the peak-to-base ratio. We find good consistency between retrieved results employing our method and visual analysis. The testing of synthetic signals shows that our algorithm performs well with SNRs higher than 4. The results demonstrate that our algorithm is simple, practical, and suited to large dataset applications.

© 2011 Optical Society of America

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