Signal processing for hyperspectral image exploitation

By Gary A. Shaw, Dimitris G. Manolakis. IEEE Signal Processing Magazine, 2002

473 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/81ffd3c368c2db369dd53175d7abb910b232ca0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Biomedical Engineering

In Media Technology

Citations in the corpus

Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis (2012, background): “The spectral response of remotely sensed data can be affected by many factors, such as differences in illumination conditions, geometric features of material surfaces, and atmospheric effects [20].”
Parametric Rao Test for Multichannel Adaptive Signal Detection (2007, methodology): “Interestingly, the parametric Rao test takes a form identical to that of the recently introduced parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) in airborne surveillance radar systems and other similar applications.”
Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, background): “The image taken by NIR-HSI also provides a spectral signature of the sample that is unique and can be used to characterize and identify any given material (16).”
Parallel Hyperspectral Image and Signal Processing [Applications Corner] (2011, background): “However, the majority of algorithms can be organized according to the following specific tasks [3]:”
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, background): “Moreover, as spectral variability needs to be addressed in supervised target detection [1], the multiplicity of possible spectra associated with the objects of interest has led to the development and application of anomaly detectors.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “This data set is generated with a mixing matrix sampled from the USGS library and with endmembers, spectral vectors, and fractional abundances given by mixtures of two Dirichlet modes with parameters [6, 25, 9] and [7, 8, 23] and mode weights of 0.”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “With respect to the spectral variability, which is brought by many factors such as incident illumination, atmospheric effects, unwanted shade and shadow, natural spectrum variation, and instrument noises [2], [4], [10],”
Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review (2012, background): “In essence, the spectral signature can be used to uniquely characterize, identify, and discriminate between classes/types of any given material(s) in each pixel of the image (Shaw and Manolakis, 2002).”
Hyperspectral Subspace Identification (2008, methodology): “Nevertheless, a similar approach has been extensively used as a dimensionality reduction tool in hyperspectral image processing [5], [47].”
Does independent component analysis play a role in unmixing hyperspectral data? (2003, background): “THE DEVELOPMENT of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection, and classification of targets activities [1]–[7].”
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification (2015, background): “These myriad of sensors serve to particularly different objectives, focusing either on obtaining quantitative measurements and estimations of geo-bio-physical variables, or on the identiﬁcation of materials by the analysis of the acquired images [1]–[3].”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “Remote sensing exploits these physical facts and deals with the acquisition of information about a scene (or specific object) at a short, medium or long distance from the target [1]–[3].”
VIS/NIR imaging application for honey floral origin determination (2017, background): “In essence, the spectral signature can be used to uniquely characterize, identify, and discriminate classes in each pixel of the image [32].”
Estimation of signal subspace on hyperspectral data (2005, unknown): “An estimate of the signal correlation matrix is given by R̂x = R̂y − R̂n, (2)”