Effect of Label Noise on the Machine-Learned Classification of Earthquake Damage
暂无分享,去创建一个
Jared Frank | Umaa Rebbapragada | James Bialas | Thomas Oommen | Timothy C. Havens | T. Oommen | T. Havens | U. Rebbapragada | J. Bialas | Jared Frank
[1] Mario Chica-Olmo,et al. An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .
[2] Bardan Ghimire,et al. An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA , 2012 .
[3] G. Foody. Assessing the accuracy of land cover change with imperfect ground reference data , 2010 .
[4] Thomas Blaschke,et al. Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[5] J. Strobl,et al. Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications , 2000 .
[6] Prashanth Reddy Marpu,et al. Geographic object-based image analysis , 2009 .
[7] M. Verleysen,et al. Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[8] R. Chhikara,et al. Linear discriminant analysis with misallocation in training samples , 1984 .
[9] Lucy Bastin,et al. The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data , 2016, ISPRS Int. J. Geo Inf..
[10] Emmanuelle Gouillart,et al. scikit-image: image processing in Python , 2014, PeerJ.
[11] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[12] Umaa Rebbapragada,et al. Object-based classification of earthquake damage from high-resolution optical imagery using machine learning , 2016 .
[13] Arno Schäpe,et al. Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .
[14] Giles M. Foody,et al. The impact of imperfect ground reference data on the accuracy of land cover change estimation , 2009 .
[15] Bin Jiang,et al. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information , 2016, ISPRS Int. J. Geo Inf..
[16] Stuart P. D. Gill,et al. A Comprehensive Analysis of Building Damage in the 12 January 2010 Mw7 Haiti Earthquake Using High-Resolution Satellite and Aerial Imagery , 2011 .
[17] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[18] Johannes R. Sveinsson,et al. Random Forests for land cover classification , 2006, Pattern Recognit. Lett..
[19] Sassan Saatchi,et al. The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest , 2000, IEEE Trans. Geosci. Remote. Sens..
[20] Haiqing Xu,et al. Urban building damage detection from very high resolution imagery using one-class SVM and spatial relations , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.
[21] Gülsen Taskin Kaya,et al. Damage Assessment of 2010 Haiti Earthquake with Post-Earthquake Satellite Image by Support Vector Selection and Adaptation , 2011 .
[22] Albert Yu-Min Lin,et al. Limitations of crowdsourcing using the EMS-98 scale in remote disaster sensing , 2014, 2014 IEEE Aerospace Conference.