Hyperspectral Data Exploitation

By Chein-I. Chang. 2007

507 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8eb1de1d4c09b4c60be7ee272564de003d0ade14

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Citations in the corpus

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].”
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, background): “However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited [8], [9].”
Deep learning classifiers for hyperspectral imaging: A review (2019, background): “…this work, we particularly focus on the topic of HSI data classification, which has received remarkable attention due its important role in land use and land cover applications (Cheng et al., 2017a), and which is currently one of the most popular techniques for HSI data exploitation (Chang, 2007).”
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis (2009, background): “The problem is complex due to the high number of spectral channels (which results in a high-dimensional feature space), the relatively small number of labeled data, the presence of different sources of noise, and the nonstationary behavior of landcover spectral signatures [2]–[4].”
Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification (2018, background): “Alternatively, chromatic aberrations could be harnessed to extract color information 32 , and incorporation of hyperspectral imaging techniques could further enhance the information captured by the device 33 .”
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016, methodology): “As for the ranking-based UBS methods, it first quantifies the importance of each band according to a certain criterion, such as nonGaussianity [19], [29], variance [25], mutual information [30], etc.”