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http://dx.doi.org/10.11108/kagis.2019.22.4.001

Dimensionality Reduction Methods Analysis of Hyperspectral Imagery for Unsupervised Change Detection of Multi-sensor Images  

PARK, Hong-Lyun (Dept. of Smart City Engineering, Youngsan University)
PARK, Wan-Yong (Agency for Defense Development)
PARK, Hyun-Chun (Agency for Defense Development)
CHOI, Seok-Keun (Dept. of Civil Engineering, Chungbuk National University)
CHOI, Jae-Wan (Dept. of Civil Engineering, Chungbuk National University)
IM, Hon-Ryang (National Geographic Information Institute)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.22, no.4, 2019 , pp. 1-11 More about this Journal
Abstract
With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.
Keywords
Unsupervised change detection; hyperspectral image; change vector analysis (CVA); principal component analysis (PCA);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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