Robustness and transferability of urban material gradients generated from Hyperspectral data

Tuesday, March 9, 2021
4:00 PM CET (Central European Time)
10:00 AM US Eastern time
Speaker: Chaonan Ji, German Aerospace Center

Sponsored by GRSS

GRSS Webinar: Robustness and transferability of urban material gradients generated from Hyperspectral data

Register here.

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. This webinar will give an overview of how to generate robust and transferable urban material gradients from Hyperspectral data.

SPEAKER’S BIO:

Chaonan Ji received the B.Sc. degree in space science and technology and the M.Sc. degree in space physics from Shandong University, Jinan, China, in 2015 and 2018, respectively. She is pursuing the Ph.D. degree in urban mapping and Earth observation with the Humboldt University of Berlin, Berlin, Germany, and the German Remote Sensing Data Center, German Aerospace Center (DLR), Weßling, Germany.

Her research interests include airborne and spaceborne hyperspectral image classification, gradient analysis, and their applications in Earth observation.

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