Attention-Based Scattering Network for Satellite Imagery

• URL: http://arxiv.org/abs/2210.12185v1
• Date: Fri, 21 Oct 2022 18:25:34 GMT
• Title: Attention-Based Scattering Network for Satellite Imagery
• Authors: Jason Stock and Chuck Anderson
• Abstract summary: We leverage the scattering to extract high-level features without additional trainable parameters. Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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