Paper Info
Title
Spectral unmixing-based post-processing for hyperspectral image classification
Abstract
Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a simple spectral unmixing-based post-processing method to improve the classification accuracies provided by supervised and semi-supervised techniques for hyperspectral image classification. The proposed approach exploits the information retrieved with spectral unmixing in order to complement the results obtained after the classification stage (which can be either supervised or semi-supervised), thus bridging the gap between unmixing and classification and exploiting both techniques in synergistic fashion for hyperspectral data interpretation. The proposed method is experimentally validated using a real hyperspectral data set collected by the Reflective Optics Spectrographic Imaging System (ROSIS). Our experimental results indicate that the proposed unmixing-based preprocessing can improve the classification results for some of the classes, particularly the most highly mixed ones, in supervised and semi-supervised scenarios using limited training samples.
Year
DOI
Venue
2013
10.1109/WHISPERS.2013.8080675
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Keywords
Field
DocType
Hyperspectral imaging,classification,spectral unmixing,semi-supervised learning
Hyperspectral image classification,Monte Carlo method,Pattern recognition,Data interpretation,Computer science,Hyperspectral imaging,Feature extraction,Preprocessor,Artificial intelligence,Probabilistic logic
Conference
ISSN
ISBN
Citations 
2158-6268
978-1-5090-1120-9
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Inmaculada Dopido1764.98
Paolo Gamba268292.97
Antonio Plaza33475262.63