Machine learning is a fast-paced emerging field that is utilized frequently in our daily lives, including google search engines, self-driving cars, natural language processing and much more. Machine learning methods excel at discovering patterns and recognizing similarities within a massively multivariate data system or one composed of thousands of variables, which is a great fit for hyperspectral and multi-spectral remote-sensing satellite imagery.

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We work on the development of machine learning based algorithms to retrieve aerosol and cloud properties from remote sensing measurements: see our recent paper, which is a part of the NASA NeMO-Net project: http://nemonet.info/

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Segal-Rozenhaimer, M., et al. (2020), Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN), Remote Sensing of Environment, 237, 111446, doi:10.1016/j.rse.2019.111446.

 

 

Developing an algorithm to detect marine stratocumulus cloud types (Marine Cellular convection) from geostationary satellites to better understand their dynamics and formation mechanisms (NASA ACMAP funded project)