Recent Advances in Techniques for Hyperspectral Image Processing

By Antonio J. Plaza, Jon Atli Benediktsson, J. Boardman, Jason Brazile, Lorenzo Bruzzone, Gustau Camps-Valls, Jocelyn Chanussot, Mathieu Fauvel, Paolo Gamba, Anthony J. Gualtieri and 3 more. 2009

1,721 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology

View Paper: https://www.semanticscholar.org/paper/e616132691824d6eec92b0eb4560b286d732582b

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In Analytical Chemistry

In Media Technology

Citations in the corpus

Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (2014, background): “It has received a lot of attention in the remote sensing literature [50]–[52].”
Spectral–spatial classification of hyperspectral images using deep convolutional neural networks (2015, methodology): “Several image processing techniques associated with machine learning algorithms have been applied to hyperspectral data classification during the last decade (Plaza et al. 2009).”
Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles (2012, methodology): “Morphological proﬁles (MP), which combine spectral and spatial information, have been shown to be effective tools for classiﬁcation of remote sensing data [1] [2] [3] [4] [5] [6].”
Sparse Representation for Target Detection in Hyperspectral Imagery (2011, background): “It has also been shown that the integration of the contextual information via composite kernels in SVM (i.e., contextual SVM) leads to an improvement in HSI classification over the traditional spectral-only SVM [24], [25].”
Use of Hyperspectral Imaging to Discriminate the Variety and Quality of Rice (2015, background): “Despite the wide application of the two techniques, spectroscopic technique only uses spectral information without consideration of spatial information and digital imaging provides only limited spectral information (Plaza et al. 2009).”
Medical hyperspectral imaging: a review (2014, methodology): “This method treats hyperspectral data as unordered listings of spectral measurements without particular spatial arrangement, 130 which may result in a salt-and-pepper look for the classification map.”
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, methodology): “For all these reasons, classifiers exploiting spatial information extracted from HS data, but also applying dimension reduction, tend to achieve better results than a purely spectral classifier applied in high-dimensional feature space [10], [15].”
Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, background): “This fusion of the spatial and spectral information is an important part for hyperspectral analyses (Plaza et al. 2009).”
Semi-supervised hyperspectral image segmentation (2009, methodology): “improve the classification accuracy, some methods have integrated spatial (contextual) information [7, 8, 10].”
Feature Mining for Hyperspectral Image Classification (2013, methodology): “For these reasons, after applying advanced signal processing techniques, hyperspectral data are extremely useful in a wide range of applications from mapping ground cover types and assessment of their condition, to target detection [4].”
Recent Developments in High Performance Computing for Remote Sensing: A Review (2011, background): “and planetary observation is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges [5].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, neutral): “during/in the last decade [20], [39]–[46].”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, background): “airborne visible infrared imaging spectrometer (AVIRIS), and multispectral infrared and visible imaging spectrometer (MIVIS), are now available for precision agriculture applications including yield estimation, fruit detection, environmental impact assessment and crop disease detection (Zhang et al.”