Abstract
Abstract. 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 α-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.
Highlights
Hyperspectral image has the potential to highlight the subtle differences between the materials of interest in the hundreds of spectral bands
ROSIS University: The first hyperspectral dataset was collected over the University of Pavia, Italy by the ROSIS airborne hyperspectral sensor in the framework of HySens project managed by DLR (German national aerospace agency)
The classifier which produces the maximum overall accuracy is known as single best classifier, and this is used as a benchmark to compare the multiple classifier system (MCS) classification results
Summary
Hyperspectral image has the potential to highlight the subtle differences between the materials of interest in the hundreds of spectral bands. The processing of hyperspectral image often comes with challenges due to its very high dimensionality and redundant information. The methods of exploring information from the hyperspectral image is still an active area of research. Among the many methods available in the literature, supervised image classification is the most common used approach for extracting information from the hyperspectral image in the form of classification map. The supervised classification of hyperspectral image is limited due to the factors such as high dimension, spectral and spatial variability, and limited available ground truth samples (Camps-Valls et al, 2014, Jimenez and Landgrebe, 1998). Determining the class, classifier and dimensionality reduction method relationship is necessary to obtain the optimal classification performance (Damodaran and Nidamanuri, 2014a)
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