Spectral Similarity Measure Using Frequency Spectrum for Hyperspectral Image Classification

Wang, Ke; Yong, Bin; Gu, Xingfa; Xiao, Pengfeng; Zhang, Xueliang

doi:10.1109/lgrs.2014.2329183

Cited by 26 publications

(6 citation statements)

References 18 publications

(12 reference statements)

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“…Commonly used spectral similarity measures include the spectral angle mapper (SAM) [41], spectral information divergence (SID) [42], spectral correlation mapper (SCM) [43], spectral gradient angle (SGA) [44], Euclidean distance (ED) [45], and SID×tan(SAM) and SID×sin(SAM) [46]. Wang et al [47,48] proposed frequency-domain-based spectral similarity measures for HSI classification. Such measures can be used for anomaly detection [49], crop monitoring [50][51][52], and land cover classification [53].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

2020

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Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

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Section: Introductionmentioning

confidence: 99%

Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

2020

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“…Therefore, an important research step for this chapter would be to identify regions within heterogeneous images that were spectrally similar, using algorithms specifically designed for hyperspectral similarity [Wang et al, 2015], so that only those pixels would be used in the spectral super-resolution algorithm. It is likely that the selected homogeneous regions will have mild residual heterogeneity, so a final addition to this chapter would be to train the machine learning techniques to be insensitive to mild heterogeneity, as it is unlikely that waters will be perfectly mixed on these scales.…”

Section: Future Work In Sub-meter Ocean Color Remote Sensingmentioning

confidence: 99%

Computational approaches for sub-meter ocean color remote sensing

O'Shea¹

2021

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“…The work in [53,54] indicates that a certain amount of low frequencies is enough to describe the original spectral signature. Using Equation (7), the discrete signal can be expressed as the sum of harmonics, namely the frequency components.…”

Section: Fourier Analysis Of the Simulated Discrete Signalmentioning

confidence: 99%

“…Thus, it indicates that we can use the frequency spectrum to analyze the availability of the existing spectral similarity measures. Based on this conclusion, Wang et al [53,54] proposed two spectral similarity measures based on the frequency domain. Wang et al [53] previously used the first few low-frequency components to produce the measure.…”

Section: Introductionmentioning

confidence: 98%

“…To balance the difference between the low-and high-frequency components, the Canberra distance is introduced to increase the contribution from the higher frequencies. In the following research [54], the frequency spectrum of the target spectral signature is taken as the normalized factor to balance the lower and higher frequency components. In this paper, we use the frequency spectrum of the spectral signatures to calculate the spectral similarity measures based on the existing measures, including the SAM, SID, SCM, ED, NED, StS and SsS measures.…”

Section: Introductionmentioning

confidence: 99%

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Application of the Frequency Spectrum to Spectral Similarity Measures

Wang

Yong

2016

Remote Sensing

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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

Cited by 26 publications

References 18 publications

Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

Computational approaches for sub-meter ocean color remote sensing

Application of the Frequency Spectrum to Spectral Similarity Measures

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