PSIque: Next Sequence Prediction of Satellite Images using a Convolutional Sequence-to-Sequence Network

Abstract

Predicting unseen weather phenomena is an important issue for disaster management. In this paper, we suggest a model for a convolutional sequence-to-sequence autoencoder for predicting undiscovered weather situations from previous satellite images. We also propose a symmetric skip connection between encoder and decoder modules to produce more comprehensive image predictions. To examine our model performance, we conducted experiments for each suggested model to predict future satellite images from historical satellite images. A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate closest prediction from the ground truth image.

Workshop on Deep Learning for Physical Sciences (DLPS 2017), NIPS 2017, Long Beach, CA, USA

References in corpus (2)

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery

Cited by in corpus (2)

• Prediction of Soil Moisture Content Based On Satellite Data and Sequence-to-Sequence Networks
• Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series