Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery-Reference-Cited by-同舟云学术

Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery

Published:2008-06 Issue:6 Volume:112 Page:2999-3011
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Chan Jonathan Cheung-Wai,Paelinckx Desiré

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference45 articles.

1. Methodology for hyperspectral band selection;Bajcsy;Photogrammetric Engineering and Remote Sensing,2004

2. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants;Bauer;Machine Learning,1998

3. Consensus theoretic classification methods;Benediktsson;IEEE Transactions on Systems, Man Cybernet,1992

4. Calibration of broad- and narrow-band spectral variables for rangeland cover component quantification;Bork;International Journal of Remote Sensing,1999

5. Bagging predictors;Breiman;Machine Learning,1996

Cited by 634 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mapping tree carbon density using sentinel 2A sensor on Google Earth Engine in Darjeeling Himalayas: Implication for tree carbon management and climate change mitigation;Physics and Chemistry of the Earth, Parts A/B/C;2024-06

2. Supraglacial lake evolution on Tracy and Heilprin Glaciers in northwestern Greenland from 2014 to 2021;Remote Sensing of Environment;2024-03

3. Permutation importance based modified guided regularized random forest in human activity recognition with smartphone;Engineering Applications of Artificial Intelligence;2024-03

4. Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery;Stochastic Environmental Research and Risk Assessment;2024-01-09

5. Land use classification in mine-agriculture compound area based on multi-feature random forest: a case study of Peixian;Frontiers in Sustainable Food Systems;2024-01-04