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Journal of Sensors
Volume 2017, Article ID 9702612, 14 pages
Research Article

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

1School of Convergence & Fusion System Engineering, Kyungpook National University, Sangju 37224, Republic of Korea
2School of Engineering and Computing Sciences, Texas A&M University-Corpus Christi, 6300 Ocean Dr., Corpus Christi, TX 78412, USA
3School of Civil Engineering, Chungbuk National University, 1, Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Republic of Korea

Correspondence should be addressed to Jaewan Choi;

Received 25 April 2017; Accepted 9 July 2017; Published 10 August 2017

Academic Editor: Hyung-Sup Jung

Copyright © 2017 Youkyung Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.