2018
DOI: 10.1109/jstars.2017.2775644
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Constrained Manifold Learning for Hyperspectral Imagery Visualization

Abstract: Abstract-Displaying the large number of bands in a hyperspectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to … Show more

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Cited by 20 publications
(14 citation statements)
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References 31 publications
(46 reference statements)
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“…Pavia Centre (Figure 1b) is a 1096 × 715, 102-band image with the same characteristics as Pavia University. In both cases, the 10th, 31st and 46th bands were used for generating the RGB representations [25].…”
Section: Hyperspectral Imagesmentioning
confidence: 99%
See 2 more Smart Citations
“…Pavia Centre (Figure 1b) is a 1096 × 715, 102-band image with the same characteristics as Pavia University. In both cases, the 10th, 31st and 46th bands were used for generating the RGB representations [25].…”
Section: Hyperspectral Imagesmentioning
confidence: 99%
“…For the last three images, the RGB representations were generated by selecting the 6th, 17th, and 36th bands [25].…”
Section: Hyperspectral Imagesmentioning
confidence: 99%
See 1 more Smart Citation
“…By feature extraction, a projection matrix is used to map the original spectral data to a feature space while holding the dominant spectral information [13]. Typical feature extraction algorithms include principal component analysis (PCA) [14], linear discriminant analysis (LDA) [15], manifold learning [16], nonnegative matrix factorization (NMF) [17] and spatial-spectral feature extraction [18].…”
Section: Introductionmentioning
confidence: 99%
“…With spectral sampling from visible to short-wave infrared region, hyperspectral image (HSI) can provide a spatial scene in hundreds of narrow contiguous spectral channels [1,2]. HSI data with high spectral resolution can provide fine spectral details for different ground objects, and they have been widely applied in many fields such as geological survey, environmental monitoring, precision agriculture, and mineral exploration [3,4].…”
Section: Introductionmentioning
confidence: 99%