2017
DOI: 10.1080/10095020.2017.1403088
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A new kernel method for hyperspectral image feature extraction

Abstract: Key laboratory of Digital earth science, institute of remote sensing and Digital earth, chinese academy of sciences, Beijing, china; b University of chinese academy of sciences, ringgold standard institution, Beijing, china; c Department of telecommunications and information processing, Ghent University, Ghent, Belgium ABSTRACTHyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the i… Show more

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Cited by 37 publications
(11 citation statements)
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“…Texture is a fundamental feature to describe image, but the majority of texture descriptors are based on regular images or regular regions and do not consider the color information. Shape is an important illustration characteristic and of the primordial feature for image content depiction [14]. Shape features are very significant features, which are very close to human perception.…”
Section: Introductionmentioning
confidence: 99%
“…Texture is a fundamental feature to describe image, but the majority of texture descriptors are based on regular images or regular regions and do not consider the color information. Shape is an important illustration characteristic and of the primordial feature for image content depiction [14]. Shape features are very significant features, which are very close to human perception.…”
Section: Introductionmentioning
confidence: 99%
“…Hyperspectral remote sensing combines imaging technology and spectroscopy technology to obtain continuous and narrow-band image data with high spectral resolution [1], which improves the ability to monitor the Earth's systems and human activities [2,3]. However, the high-dimensional data obtained by the hyperspectral sensor are challenging to analyse and apply [4,5]. In hyperspectral image (HSI) classification, with limited training samples, the classifier performance first improves as the dimension increases but degrades when the dimension is higher than an optimal value (the Hughes phenomenon) [6,7].…”
Section: Introductionmentioning
confidence: 99%
“…Because of the kernel functions in KMNF, the computational complexity increased and the execution efficiency was reduced [38,40,41]. Although KMNF is widely used in non-linear feature extraction, it has been shown that KMNF cannot produce the expected results because of the inaccurate noise estimation [5,26,42,43]. The conventional MNF noise estimation (MNF-NE) and KMNF noise estimation (KMNF-NE) utilize the residual of a local regression in 3 × 3 neighbourhood pixels to a paraboloid or a plane, which depends heavily on the relationship between adjacent pixels [38,44].…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the hundreds of bands make HSI data possess high dimensionality which will cause the Hughes phenomenon with the traditional classification methods (An et al 2019;Mohanty, Happy, and Routray 2019b). For the Hughes phenomenon, the classification results will improve and then decrease with the increasing of dimensionality under limited samples (Hang and Liu 2018;Song et al 2019;Zhao et al 2017). Although HSI generates better discriminant performance for similar materials than the traditional image, it also faces more challenges to process HSI with the above problems.…”
Section: Introductionmentioning
confidence: 99%