Semi-supervised change detection via Gaussian processes

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 2, Issue , 2009, Pages 996-999

  Chen, Keming ^a   Huo, Chunlei ^a   Zhou, Zhixin ^a,b   Lu, Hanqing ^a   Cheng, Jian ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b Beijing Institute of Remote Sensing   (China)

Author keywords

Change detection; Gaussian process; Semi supervised learning

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GEOLOGY; MACHINE LEARNING; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

CHANGE DETECTION; GAUSSIAN PROCESSES; MULTI-TEMPORAL REMOTE SENSING; PROBABILISTIC APPROACHES; PROBABILISTIC FRAMEWORK; REMOTE SENSING IMAGES; SEMI- SUPERVISED LEARNING; TRANSDUCTIVE LEARNING;

REMOTE SENSING;

EID: 77951138098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2009.5418269     Document Type: Conference Paper

References (5)

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2. F. Bovolo, L. Bruzzone, M. Marconcini. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE trans. Geoscience and Remote Sensing, 46(7): 2070-2082, 2008.
3. N. D. Lawrence and M. I. Jordan. Semi-supervised learning via Gaussian processes. L. Saul, Y. Weiss and L. Bouttou, Advances in Neural Information Processing Systems, 2005, Cambridge, MA, MIT Press.
4. C. E. Rasmussen and C.K.I. Williams. Gaussian Process for Machine Learning. Cambridge. Massachusetts: The MIT Press, 2006.
5. http://www.cs.cornell.edu/People/tj/.