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Remote Sens. 2016, 8(4), 344; doi:10.3390/rs8040344

Application of the Frequency Spectrum to Spectral Similarity Measures

1,2,* and 1,*
1
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2
Department of Geographical Information Science, Hohai University, Nanjing 210098, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Andras Jung, Magaly Koch and Prasad S. Thenkabail
Received: 18 January 2016 / Revised: 2 March 2016 / Accepted: 11 March 2016 / Published: 20 April 2016
View Full-Text   |   Download PDF [1698 KB, uploaded 20 April 2016]   |  

Abstract

Several frequency-based spectral similarity measures, derived from commonly-used ones, are developed for hyperspectral image classification based on the frequency domain. Since the frequency spectrum (magnitude spectrum) of the original signature for each pixel from hyperspectral data can clearly reflect the spectral features of different types of land covers, we replace the original spectral signature with its frequency spectrum for calculating the existing spectral similarity measure. The frequency spectrum is symmetrical around the direct current (DC) component; thus, we take one-half of the frequency spectrum from the DC component to the highest frequency component as the input signature. Furthermore, considering the fact that the low frequencies include most of the frequency energy, we can optimize the classification result by choosing the ratio of the frequency spectrum (from the DC component to the highest frequency component) involved in the calculation. In our paper, the frequency-based measures based on the spectral gradient angle (SAM), spectral information divergence (SID), spectral correlation mapper (SCM), Euclidean distance (ED), normalized Euclidean distance (NED) and SID × sin(SAM) (SsS) measures are called the F-SAM, F-SID, F-SCM, F-ED, F-NED and F-SsS, respectively. In the experiment, three commonly-used hyperspectral remote sensing images are employed as test data. The frequency-based measures proposed here are compared to the corresponding existing ones in terms of classification accuracy. The classification results by parameter optimization are also analyzed. The results show that, although not all frequency-based spectral similarity measures are better than the original ones, some frequency-based measures, such as the F-SsS and F-SID, exhibit a relatively better performance and have more robust applications than the other spectral similarity measures. View Full-Text
Keywords: hyperspectral; spectral similarity measure; frequency spectrum; Fourier transform hyperspectral; spectral similarity measure; frequency spectrum; Fourier transform
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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. (CC BY 4.0).

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Wang, K.; Yong, B. Application of the Frequency Spectrum to Spectral Similarity Measures. Remote Sens. 2016, 8, 344.

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