The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Xueliang Zhang - One of the best experts on this subject based on the ideXlab platform.
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Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Ke Wang, Bin Yong, Xingfa Gu, Pengfeng Xiao, Xueliang ZhangAbstract:A novel spectral similarity measure approach, which is named spectral Frequency Spectrum difference (SFSD), is proposed for hyperspectral image classification based on the Frequency Spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the Frequency Spectrum. Therefore, the spectral similarity is defined as the Frequency Spectrum's difference between the target and reference signatures. The Frequency Spectrum analysis in this study suggests that the magnitude values of the first few low-Frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low- and high-Frequency components, the Frequency Spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.
Ke Wang - One of the best experts on this subject based on the ideXlab platform.
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Application of the Frequency Spectrum to Spectral Similarity Measures
Remote Sensing, 2016Co-Authors: Ke Wang, Bin YongAbstract: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.
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Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Ke Wang, Bin Yong, Xingfa Gu, Pengfeng Xiao, Xueliang ZhangAbstract:A novel spectral similarity measure approach, which is named spectral Frequency Spectrum difference (SFSD), is proposed for hyperspectral image classification based on the Frequency Spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the Frequency Spectrum. Therefore, the spectral similarity is defined as the Frequency Spectrum's difference between the target and reference signatures. The Frequency Spectrum analysis in this study suggests that the magnitude values of the first few low-Frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low- and high-Frequency components, the Frequency Spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.
Bin Yong - One of the best experts on this subject based on the ideXlab platform.
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Application of the Frequency Spectrum to Spectral Similarity Measures
Remote Sensing, 2016Co-Authors: Ke Wang, Bin YongAbstract: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.
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Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Ke Wang, Bin Yong, Xingfa Gu, Pengfeng Xiao, Xueliang ZhangAbstract:A novel spectral similarity measure approach, which is named spectral Frequency Spectrum difference (SFSD), is proposed for hyperspectral image classification based on the Frequency Spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the Frequency Spectrum. Therefore, the spectral similarity is defined as the Frequency Spectrum's difference between the target and reference signatures. The Frequency Spectrum analysis in this study suggests that the magnitude values of the first few low-Frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low- and high-Frequency components, the Frequency Spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.
Zhongqi Wang - One of the best experts on this subject based on the ideXlab platform.
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A prediction model for Frequency Spectrum of blast‐induced seismic wave in viscoelastic medium
Geophysical Prospecting, 2017Co-Authors: Chenglong Yu, Zhongqi WangAbstract:Summary A prediction model for Frequency Spectrum of blast-induced seismic waves is established. The effect of explosive sources is considered in this model. Our model implies that the Frequency Spectrum of blast-induced seismic wave is mainly influenced by the initial pressure and the adiabatic exponent of explosives. The dominant Frequency increases with the decreasing of initial pressure or the increasing of adiabatic exponent. In addition, this prediction model is verified by the experiment. The error of the dominant Frequency is 4%-6%. It is indicate that the proposed model in this paper can reasonably predict the Frequency Spectrum of blast-induced seismic waves, and then we can provide a better Frequency Spectrum by optimizing the explosion source. This article is protected by copyright. All rights reserved
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A prediction model for Frequency Spectrum of blast-induced seismic wave in viscoelastic medium
Geophysical Prospecting, 2017Co-Authors: Chenglong Yu, Zhongqi WangAbstract:Summary A prediction model for Frequency Spectrum of blast-induced seismic waves is established. The effect of explosive sources is considered in this model. Our model implies that the Frequency Spectrum of blast-induced seismic wave is mainly influenced by the initial pressure and the adiabatic exponent of explosives. The dominant Frequency increases with the decreasing of initial pressure or the increasing of adiabatic exponent. In addition, this prediction model is verified by the experiment. The error of the dominant Frequency is 4%-6%. It is indicate that the proposed model in this paper can reasonably predict the Frequency Spectrum of blast-induced seismic waves, and then we can provide a better Frequency Spectrum by optimizing the explosion source. This article is protected by copyright. All rights reserved
Divakar Viswanath - One of the best experts on this subject based on the ideXlab platform.
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The Wright-Fisher site Frequency Spectrum as a perturbation of the coalescent's.
Theoretical Population Biology, 2018Co-Authors: Andrew Melfi, Divakar ViswanathAbstract:The first terms of the Wright–Fisher (WF) site Frequency Spectrum that follow the coalescent approximation are determined precisely, with a view to understanding the accuracy of the coalescent approximation for large samples. The perturbing terms show that the probability of a single mutant in the sample (singleton probability) is elevated in WF but the rest of the Frequency Spectrum is lowered. A part of the perturbation can be attributed to a mismatch in rates of merger between WF and the coalescent. The rest of it can be attributed to the difference in the way WF and the coalescent partition children between parents. In particular, the number of children of a parent is approximately Poisson under WF and approximately geometric under the coalescent. Whereas the mismatch in rates raises the probability of singletons under WF, its offspring distribution being approximately Poisson lowers it. The two effects are of opposite sense everywhere except at the tail of the Frequency Spectrum. The WF Frequency Spectrum begins to depart from that of the coalescent only for sample sizes that are comparable to the population size. These conclusions are confirmed by a separate analysis that assumes the sample size n to be equal to the population size N. Partly thanks to the canceling effects, the total variation distance of WF minus coalescent is 0.12∕logN for a population sized sample with n=N, which is only 1% for N=2A—104. The coalescent remains a good approximation for the site Frequency Spectrum of-large samples.
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The Wright-Fisher Site Frequency Spectrum as a Perturbation of the Coalescent's
bioRxiv, 2018Co-Authors: Andrew Melfi, Divakar ViswanathAbstract:The first terms of the Wright-Fisher (WF) site Frequency Spectrum that follow the coalescent approximation are determined precisely, with a view to understanding the accuracy of the coalescent approximation for large samples. The perturbing terms show that the probability of a single mutant in the sample (singleton probability) is elevated in WF but the rest of the Frequency Spectrum is lowered. A part of the perturbation can be attributed to a mismatch in rates of merger between WF and the coalescent. The rest of it can be attributed to the difference in the way WF and the coalescent partition children between parents. In particular, the number of children of a parent is approximately Poisson under WF and approximately geometric under the coalescent. Whereas the mismatch in rates raises the probability of singletons under WF, its offspring distribution being approximately Poisson lowers it. The two effects are of opposite sense everywhere except at the tail of the Frequency Spectrum. The WF Frequency Spectrum begins to depart from that of the coalescent only for sample sizes that are comparable to the population size. These conclusions are confirmed by a separate analysis that assumes the sample size n to be equal to the population size N. Partly thanks to the canceling effects, the total variation distance of WF minus coalescent is 0.12/ log(N) for a population sized sample with n = N, which is only 1% for N = 2 x 104.
