Spectral-spatial approaches for hyperspectral data classification
Date
2014
Authors
Roy, Sathi
University of Lethbridge. Faculty of Arts and Science
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
Classification of hyperspectral data is very challenging and mapping of land cover is one of
its applications. Improving the classification accuracy and computation time of hyperspectral
data were achieved incorporating contextual information in combination with spectral information for correcting classification errors along class boundaries and within class. In
the proposed method, the original hyperspectral image was first classified using the Support
Vector Machine (SVM) classifier, followed by the Markov Random Field (MRF) approach
applied to the boundary areas and Unsupervised Extraction and Classification of Homogeneous Objects (UnECHO) classifier used for the interior parts of regions to produce the final classification map. In this study two agricultural (Hyperion and AVIRIS) and one
urban (ROSIS) datasets were used. Investigations of the spectral and various contextual
approaches including feature reduction show that the SVM-MRF method with grid search
works best for all of the datasets. The highest overall accuracy of 97.35% was achieved for
the urban dataset.
Description
Keywords
Spectral-Spatial Classification , Image Classification , Support Vector Machine (SVM) , Markov Random Field (MRF) , Unsupervised Extraction and Classification for Homogenous Objects (UnECHO) , SVM-Recursive Feature Elimination (SVM-RFE) , Correlation based Feature Selection (CFS) , Minimum-Redundancy–Maximum-Relevance (mRMR) , Remote Sensing , Erosion Technique , Grid Search