Application of the Frequency Spectrum to Spectral Similarity Measures
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 re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-04-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/4/344 |
| _version_ | 1798025760199409664 |
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| author | Ke Wang Bin Yong |
| author_facet | Ke Wang Bin Yong |
| author_sort | Ke Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:24:07Z |
| format | Article |
| id | doaj.art-4da37eaec0d74249ba04fab42fd6a245 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T18:24:07Z |
| publishDate | 2016-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4da37eaec0d74249ba04fab42fd6a2452022-12-22T04:09:41ZengMDPI AGRemote Sensing2072-42922016-04-018434410.3390/rs8040344rs8040344Application of the Frequency Spectrum to Spectral Similarity MeasuresKe Wang0Bin Yong1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaSeveral 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.http://www.mdpi.com/2072-4292/8/4/344hyperspectralspectral similarity measurefrequency spectrumFourier transform |
| spellingShingle | Ke Wang Bin Yong Application of the Frequency Spectrum to Spectral Similarity Measures Remote Sensing hyperspectral spectral similarity measure frequency spectrum Fourier transform |
| title | Application of the Frequency Spectrum to Spectral Similarity Measures |
| title_full | Application of the Frequency Spectrum to Spectral Similarity Measures |
| title_fullStr | Application of the Frequency Spectrum to Spectral Similarity Measures |
| title_full_unstemmed | Application of the Frequency Spectrum to Spectral Similarity Measures |
| title_short | Application of the Frequency Spectrum to Spectral Similarity Measures |
| title_sort | application of the frequency spectrum to spectral similarity measures |
| topic | hyperspectral spectral similarity measure frequency spectrum Fourier transform |
| url | http://www.mdpi.com/2072-4292/8/4/344 |
| work_keys_str_mv | AT kewang applicationofthefrequencyspectrumtospectralsimilaritymeasures AT binyong applicationofthefrequencyspectrumtospectralsimilaritymeasures |