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.
Highlights
Hyperspectral data can provide hundreds of contiguous bands for spectral analysis simultaneously, and in a hyperspectral image, more details are available to describe the spectral information of ground objects than in a multi-spectral image
We evaluated the classification accuracy of the proposed frequency-based measures, including F-SAM, F-spectral information divergence (SID), F-spectral correlation mapper (SCM), F-Euclidean distance (ED), F-normalized Euclidean distance (NED) and F-SID × sin(SAM) (SsS)
We have proposed several frequency-based spectral similarity measures based on commonly-used ones, including SAM, SID, SCM, ED, NED and SsS, by using the frequency spectrum of each pixel’s original spectral signature in the hyperspectral data
Summary
Hyperspectral data can provide hundreds of contiguous bands for spectral analysis simultaneously, and in a hyperspectral image, more details are available to describe the spectral information of ground objects than in a multi-spectral image. The hyperspectral data can be regarded as a spectral cube where the third dimensionality is the spectral domain due to the capability of continuous spectral capture. A problem arises in hyperspectral image classification (e.g., the Hughes phenomenon [1]). As the high dimensionality of hyperspectral data increases, the number of training samples for the classifier increases. In order to mitigate the influence of the Hughes phenomenon, dimensionality reduction is usually adopted before image classification. Feature extraction is important to dimensionality reduction for hyperspectral image analysis. Principal component analysis (PCA) [1,2] is still the commonly-used
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