TY - GEN
T1 - The spectral-spatial classification of hyperspectral images based on Hidden Markov Random Field and its Expectation-Maximization
AU - Ghamisi, Pedram
AU - Benediktsson, Jon Atli
AU - Ulfarsson, Magnus O.
PY - 2013
Y1 - 2013
N2 - In this work, a new framework for accurate classification of hyperspectral images is proposed. The new method is based on Hidden Markov Random Field and its Expectation Maximization (HMRF-EM) and Support Vector Machine (SVM) classifier. In order to preserve edges in final map, the Sobel edge detector is used. Result confirms that the combination of the spectral and spatial information can significantly improve results compared to the standard SVM method.
AB - In this work, a new framework for accurate classification of hyperspectral images is proposed. The new method is based on Hidden Markov Random Field and its Expectation Maximization (HMRF-EM) and Support Vector Machine (SVM) classifier. In order to preserve edges in final map, the Sobel edge detector is used. Result confirms that the combination of the spectral and spatial information can significantly improve results compared to the standard SVM method.
KW - Hidden Markov Random Field
KW - hyperspectral image analysis
KW - image segmentation
UR - https://www.scopus.com/pages/publications/84894272775
U2 - 10.1109/IGARSS.2013.6721358
DO - 10.1109/IGARSS.2013.6721358
M3 - Conference contribution
SN - 9781479911141
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1107
EP - 1110
BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
T2 - 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Y2 - 21 July 2013 through 26 July 2013
ER -