Attention-Based Scattering Network for Satellite Imagery (Papers Track)

Jason Stock (Colorado State University); Charles Anderson (Colorado State University)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite
Climate Science & Modeling Earth Observation & Monitoring Interpretable ML


Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy model is of utmost importance to forecasters. Neural networks show promise, yet suffer from unintuitive computations, fusion of high-level features, and may be limited by the quantity of available data. In this work, we leverage the scattering transform to extract high-level features without additional trainable parameters and introduce a separation scheme to bring attention to independent input channels. Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery.

Recorded Talk (direct link)