An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles
Joint Authors
Han, Youkyung
Choi, Jaewan
Chang, Anjin
Choi, Seokkeun
Park, Honglyun
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-10
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Han, Youkyung& Chang, Anjin& Choi, Seokkeun& Park, Honglyun& Choi, Jaewan. 2017. An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles. Journal of Sensors،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1187711
Modern Language Association (MLA)
Han, Youkyung…[et al.]. An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles. Journal of Sensors No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1187711
American Medical Association (AMA)
Han, Youkyung& Chang, Anjin& Choi, Seokkeun& Park, Honglyun& Choi, Jaewan. An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1187711
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187711