Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2019, Pages 971-974

  Feng, Wei ^a   Huang, Wenjiang ^a,b   Dauphin, Gabriel ^c   Xia, Junshi ^d   Quan, Yinghui ^e   Ye, Huichun ^a,b   Dong, Yingying ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b Key Laboratory of the Earth Observation of Hainan Province   (China)

^c UNIVERSITÉ PARIS 13   (France)

^d Disaster Resilience Science Team   (Japan)

^e XIDIAN UNIVERSITY   (China)

Author keywords

classification; ensemble margin; hyperspectral image; Random forest; semi supervised learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; GEOLOGY; HYPERSPECTRAL IMAGING; ITERATIVE METHODS; PROBABILITY; RANDOM FORESTS; REMOTE SENSING; SEMI-SUPERVISED LEARNING; SPECTROSCOPY;

ENSEMBLE MARGIN; ENSEMBLE MODELING; LIMITED TRAINING DATA; SEMI-SUPERVISED; TRAINING SETS;

IMAGE CLASSIFICATION;

EID: 85077687279     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2019.8898415     Document Type: Conference Paper

References (9)

1. X. Ma, H. Wang, and J. Wang, "Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning, " ISPRS Journal of Photogrammetry and Remote Sensing, vol. 120, pp. 99-107, 2016.
2. J. Xia, N. Falco, J. A. Benediktsson, J. Chanussot, and P. Du, "Class-separation-based rotation forest for hyperspectral image classification, " IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 4, pp. 584-588, 2016.
3. W. Feng and W. Bao, "Weight-based rotation forest for hyperspectral image classification, " IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 11, pp. 2167-2171, Nov 2017.
4. L. Breiman, "Random forests, " Machine Learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
5. S. Bernard, S. Adam, and L. Heutte, "Dynamic random forests, " Pattern Recognition Letters, vol. 33, no. 12, pp. 1580-1586, 2012.
6. W. Feng, W. Huang, and J. Ren, "Class imbalance ensemble learning based on the margin theory, " Applied Sciences, vol. 8, no. 815, 2018.
7. B. Fu, Y. Wang, A. Campbell, Y. Li, B. Zhang, S. Yin, Z. Xing, and X. Jin, "Comparison of object-based and pixel-based random forest algorithm for wetland vegetation mapping using high spatial resolution gf-1 and sar data, " Ecological Indicators, vol. 73, pp. 105-117, 2017.
8. F. Ctanovas-Garctia, F. Alonso-Sarrtia, F. Gomariz-Castillo, and F. Onate-Valdivieso, "Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery, " Computers & Geosciences, vol. 103, pp. 1-11, 2017.
9. W. Feng and S. Boukir, "Class noise removal and correction for image classification using ensemble margin, " in 2015 IEEE International Conference on Image Processing (ICIP), Sept 2015, pp. 4698-4702.