Title :
Superpixel-based Markov random field for classification of hyperspectral images
Author :
Shanshan Li ; Xiuping Jia ; Bing Zhang
Author_Institution :
Inst. of Remote Sensing & Digital Earth, Beijing, China
Abstract :
The paper presents a supervised classification method based on superpixels and Markov random field (MRF). Hyperspectral image is over-segmented into superpixels that are as basic unit of Markov random field instead of operating at the pixel level. Adaptive weight coefficient is introduced to determine contextual relationship between superpixels. Support vector machines are implemented for better estimation of spectral contribution to this approach. An experiment of real hyperspectral image reveals efficient performance.
Keywords :
Markov processes; geophysical image processing; hyperspectral imaging; image classification; image segmentation; random processes; support vector machines; MRF; adaptive weight coefficient; hyperspectral image classification; image oversegmentation; spectral contribution estimation; superpixel-based Markov random field; supervised classification method; support vector machine; Accuracy; Hyperspectral imaging; Image classification; Markov random fields; Support vector machines; Hyperspectral; MRF; classification; superpixel;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723581
