Superpixel-based active learning for the classification of hyperspectral images

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

Volumn 2015-June, Issue , 2015, Pages

  Sun, Zhongyi ^a   Chi, Mingmin ^a,b  

^a FUDAN UNIVERSITY   (China)

^b SECOND INSTITUTE OF OCEANOGRAPHY   (China)

Author keywords

Active learning; Diversity; hyperspectral images; remote sensing; superpixel

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL EFFICIENCY; HYPERSPECTRAL IMAGING; INDEPENDENT COMPONENT ANALYSIS; ITERATIVE METHODS; PIXELS; REMOTE SENSING; SPECTROSCOPY; SUPERPIXELS;

ACTIVE LEARNING; CLASSIFICATION ACCURACY; DIVERSITY; REMOTE SENSING IMAGES; REPRESENTATIVE PATTERNS; SPECTRAL SIMILARITY; TRAINING DATASET; UNLABELED SAMPLES;

IMAGE CLASSIFICATION;

EID: 85039155184     PISSN: 21586276     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2015.8075388     Document Type: Conference Paper

References (9)

1. B. Demir, C. Persello, L. Bruzzone, "Batch-mode active-learning methods for the interactive classification of remote sensing images, " IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 1014-1031, 2011.
2. D. Tuia, M. Volpi, L. Copa, M. Kanevski, J. Munoz-Mari, "A survey of active learning algorithms for supervised remote sensing image classification, " IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 3, pp. 606-617, 2011.
3. S. Patra and L. Bruzzone, "A fast cluster-assumption based active-learning technique for classification of remote sensing images, " IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 5, pp. 1617-1626, 2011.
4. D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, andW. J. Emery, "Active learning methods for remote sensing image classification, " IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 7, pp. 2218-2232, 2009.
5. C. Campbell, N. Cristianini, A. Smola, et al., "Query learning with large margin classifiers, " in International Conference on Machine Learning (ICML), 2000, pp. 111-118.
6. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines, " in International Conference on Machine Learning (ICML), 2000, pp. 839-846.
7. K. Brinker, "Incorporating diversity in active learning with support vector machines, " in International Conference on Machine Learning (ICML), vol. 3, 2003, pp. 59-66.
8. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk, "Slic superpixels compared to state-of-theart superpixel methods, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, 2012.
9. A. Vlachos, "A stopping criterion for active learning, " Computer Speech & Language, vol. 22, no. 3, pp. 295-312, 2008.