Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image
Other literature type
Bhushan, D. B.
Nidamanuri, R. R.
(issn: 2194-9034, eissn: 2194-9034)
arxiv: Computer Science::Computer Vision and Pattern Recognition
Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised
classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification
methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the
potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral
information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information
along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology
to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut
(GC) method. The problem is modelled as the energy minimization problem and solved using -expansion based graph cut method.
Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces
good quality classification map.