@inproceedings{ed62e7f405eb422583005be7cca0a1cc,
title = "Semi-supervised Conditional Random Field for hyperspectral remote sensing image classification",
abstract = "Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM), after extended into the composite kernel type, is defined as the association potential. And the Potts model is utilized as the interaction potential. The ssCRF is evaluated on the two benchmarks and the results show the effectiveness of ssCRF.",
keywords = "CRF, classification, hyperspectral, remote sensing, semi-supervised",
author = "Junfeng Wu and Zhiguo Jiang and Haopeng Zhang and Bowen Cai and Quanmao Wei",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",
year = "2016",
month = nov,
day = "1",
doi = "10.1109/IGARSS.2016.7729675",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2614--2617",
booktitle = "2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings",
address = "美国",
}