Hyperspectral Imaging

By Chein-I. Chang. Springer US, 2003

1,010 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology

View Paper: https://www.semanticscholar.org/paper/bf6bf50d1b2d1a81d23f84c3cacd1ac509270a55

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In Analytical Chemistry

In Media Technology

Citations in the corpus

Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis (2018, background): “As one of the most economically important fruit, strawberry is popularly eaten fresh, processed into jams, and flavored for juices (Elmasry et al. 2007).”
A review of neural networks in plant disease detection using hyperspectral data (2018, methodology): “Hyperspectral imaging has been used for various applications such as detection, classification, discrimination, identification, and characterization [29,30].”
Generative Adversarial Networks for Hyperspectral Image Classification (2018, background): “tion for earth observation, which brings both challenges and opportunities to develop new data processing techniques [1].”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “ieee Geoscience and remote sensinG maGazine december 2017 54 ABUNDANCE ESTIMATION Once the endmember signatures have been derived, different strategies can be used to estimate their fractional abundances [157].”
A new deep convolutional neural network for fast hyperspectral image classification (2017, background): “These kinds of images collect information corresponding to large observation areas on the surface of the Earth, using dozens/hundreds of contiguous spectral bands (Chang, 2003), thus creating a three-dimensional data cube with size significantly larger than traditional remotely sensed images.”
Sparse Unmixing of Hyperspectral Data (2011, methodology): “Several analytical tools have been developed for remotely sensed hyperspectral data processing in recent years, covering topics like dimensionality reduction, classification, data compression, or spectral unmixing [3], [4].”
Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment (2012, background): “In this respect, basic literature can be found in Chang (2003) and Grahn and Geladi (2007).”
Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas (2009, background): “Consequently, in the spectral domain, pixels are vectors where each component contains specific wavelength information provided by a particular channel [2].”
Recent Advances in Techniques for Hyperspectral Image Processing (2009, background): “Second, the sum of abundances for a givenpixelmust be unity (Chang, 2003).”
Vertex component analysis: a fast algorithm to unmix hyperspectral data (2005, methodology): “A modified version of this method called noise-whitened HFC (NWHFC) includes a noise-whitening process as preprocessing step to remove the second-order statistical correlation [17].”
Techniques and applications of hyperspectral image analysis (2007, background): “There are also some interesting books from the remote sensing field (Chang, 2003; Varhsney and Arora, 2004).”
Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests (2009, methodology): “The major steps in the new approach are outlined with spectral detection algorithms that already exist today (Roberts et al. 1998; Chang 2003), but only if the probability is high that the species exists in the spectranomic database (Figure 5, steps 5–6).”
Examination of the quality of spinach leaves using hyperspectral imaging (2013, background): “Researchers are often interested in finding the most relevant few wavelengths that 8 could influence the quality evaluation of the product (ElMasry et al., 2007).”