Detection of transverse cirrus bands in satellite imagery using deep learning. (September 2018) - Miller, Jeffrey; Nair, Udaysankar; Ramachandran, Rahul; Maskey, Manil
Abstract: We demonstrate the viability of using a convolutional neural network (CNN) for facial recognition of meteorological phenomena in satellite imagery. Transfer learning was used to fine tune the widely used VGG-16 network architecture and allow the network to successfully detect (94% accuracy) the presence of transverse cirrus bands (TCBs) in NASA MODIS and VIIRS satellite browse imagery. The CNN exhibited better performance compared to a random forest classifier (84% accuracy) and was further validated by applying it to NASA satellite browse imagery in order to create a multi-year (2013–2015) global heat map of TCB occurrence. The annual heat map shows spatial patterns that are consistent with known mechanisms for the generation of TCBs, providing confidence in the CNN classifications. Our study shows that CNNs are well suited for meteorological phenomena detection due to their generalization capabilities and strong performance. An immediate application of our work intends to enable phenomena-based search of big satellite imagery databases. With additional modifications, the CNN could be utilized for other applications such as providing situational awareness to operational forecasters or developing phenomena specific climatologies. Highlights: We trained a deep CNN to detect transverse cirrus bands (TCB) in satellite imagery. Transfer learning was utilized to allow training on a small dataset. Network performed with an accuracy of 93.9% on a test set of 1098 images. Evaluated network on a years worth (288, 000 images) of GIBS browse imagery. Locations where TCB are identified are consistent with findings from manual analysis.