文章基本信息

标题：Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image
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作者：D. B. Bhushan ; R. R. Nidamanuri
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2014
卷号：XL-8
页码：683-688
DOI：10.5194/isprsarchives-XL-8-683-2014
出版社：Copernicus Publications
摘要：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.
关键词：Hyperspectral image classification; Multiple classifier system; Spectral spatial classification; Random subspace method