| DISCRIMINATION AEROSOL FORM CLOUDS USING CATS-ISS LIDAR OBSERVATIONS BASED ON RANDOM FOREST AND SVM ALGORITHMS OVER THE EASTERN PART OF MIDDLE EAST |
| Paper ID : 1026-SMPR |
| Authors: |
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Foad Brakhasi *, Mohammad Hajeb, Fatemeh Fouladinejad Remote Sensing and GIS Center, University of Shahid Beheshti, Tehran, Iran |
| Abstract: |
| Aerosols and Clouds play an important role in the Earth's environment, climate change and climate models. The Cloud-Aerosol Transport System (CATS) as a lidar remote sensing instrument, from the International Space Station (ISS), provides range-resolved profile measurements of atmospheric aerosols and clouds. Discrimination aerosols from clouds have always been a challenges task in the classification of space-born lidars. In this study, two algorithms including Random Forest (RF) and Support Vector Machine (SVM) were used to tackle the problem in a Nighttime lidar data from 18 October 2016 which passes form the western part of Iran. The procedure includes 3 stages preprocessing (improving the signal to noise, generating features, taking training sample), classification (implementing RF and SVM), and postprocessing (correcting misleading classification). Finally, the result of classifications of two algorithms (RF-SVM) was compared against ground truth samples, Vertical Feature Mask (VFM) of CATS product indicated 0.96-0.94 and 0.88-0.88 respectively. Also, it should be mentioned that a kappa accuracy 0.88 was acquired when we compared VFM against our ground truth samples. Moreover, a visual comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and RGB products demonstrating that clouds and aerosol can be well detected and discriminated. |
| Keywords: |
| Aerosol, Cloud, CATS-ISS, Classification, Random Forest, SVM, Machine Learning |
| Status : Conditional Accept (Oral Presentation) |
