Earticle

초록

영어

Aerosol optical depth (AOD) is an important quantity parameter to study the Earth’s radiation balance, climate change and environment protection. For estimating AOD by a data mining method, the synchronized records by combing satellite observed information from MOderate Resolution Imaging Spectroradiometer (MODIS) equipment with the ground-based accurate measurements of AOD from Aerosol Robotic NETwork (AERONET) work as driving attributes and prediction targets, respectively. However, compared with the number of high-dimensional remote sensing attributes, the total number of spatial-temporal collocated MODIS-AERONET observations during a couple of years is relatively not large enough for estimation modeling. It leads to unstable feature selection subsets and drops the AOD estimation accuracy. In this paper, we propose a novel ensemble approach by aggregating multiple AOD estimators. Each estimator is modeled based on features selected from remote sensing attributes by using a subsampling strategy with instance perturbation. The ensemble approach provides aggregated retrievals of AOD with higher accuracy, while also providing an estimation of retrieval uncertainty. We conducted experiments to evaluate the empirical performance of the proposed approach on two years (2009-2011) of MODIS data over 197 global AERONET sites. The encouraging results clearly showed that aggregation of estimators modeled by multiple feature selection subsets leads to accuracy improvements and uncertainty reduction in AOD retrievals.

목차

Abstract
 1. Introduction
 2. Construction of an Ensemble Estimator
  2.1. Feature Selection Techniques
  2.2. Measure Feature Selection Stability with Instance Perturbation
  2.3. Construction of an Ensemble Estimator
  2.4. Regression Accuracy Measures
 3. Experimental Results
  3.1. MODIS-AERONET Collocated Data Sets
  3.2. Feature Selection Stability
  3.3. Construction of an Ensemble Estimator
 4. Conclusions and Future Work
 Acknowledgments
 References

키워드

저자

• Bo Han [ International School of Software, Wuhan University, Wuhan, 430079, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605