Paper Info
Title
Semi-supervised learning for integration of aerosol predictions from multiple satellite instruments
Abstract
Aerosol Optical Depth (AOD), recognized as one of the most important quantities in understanding and predicting the Earth's climate, is estimated daily on a global scale by several Earth-observing satellite instruments. Each instrument has different coverage and sensitivity to atmospheric and surface conditions, and, as a result, the quality of AOD estimated by different instruments varies across the globe. We present a method for learning how to aggregate AOD estimations from multiple satellite instruments into a more accurate estimation. The proposed method is semi-supervised, as it is able to learn from a small number of labeled data, where labels come from a few accurate and expensive ground-based instruments, and a large number of unlabeled data. The method uses a latent variable to partition the data, so that in each partition the expert AOD estimations are aggregated in a different, optimal way. We applied the method to combine AOD estimations from 5 instruments aboard 4 satellites, and the results indicate that it can successfully exploit labeled and unlabeled data to produce accurate aggregated AOD estimations.
Year
Venue
Keywords
2013
IJCAI
Semi-supervised learning,different instrument,expert AOD estimation,Earth-observing satellite instrument,AOD estimation,proposed method,multiple satellite instrument,accurate aggregated AOD estimation,different coverage,aerosol prediction,unlabeled data,accurate estimation,aggregate AOD estimation
Field
DocType
Citations 
Small number,Optical depth,Semi-supervised learning,Computer science,Remote sensing,Aerosol,Surface conditions,Latent variable,Artificial intelligence,Labeled data,Satellite,Simulation,Machine learning
Conference
4
PageRank 
References 
Authors
0.50
6
3
Name
Order
Citations
PageRank
Nemanja Djuric135225.83
Lakesh Kansakar260.92
Slobodan Vucetic363756.38