Hyperspectral subpixel target detection using the linear mixing model

By Dimitris G. Manolakis, Christina Siracusa, Gary A. Shaw. IEEE Transactions on Geoscience and Remote Sensing, 2001

405 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/d7c35c7fbefbb180ec4dea4e4d1fcd02d9f7483b

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Citations in the corpus

Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries (2004, methodology): “Encouraged by the similarity of hyperspectral data to time-series data, algorithms have borrowed from the radar literature [15], [16] to achieve levels of exactitude that were otherwise unattainable for previous remote sensing endeavors.”
Hyperspectral Subspace Identification (2008, background): “To be physically meaningful [46], abundance fractions are subject to nonnegativity and full additivity constraints, i.”
Vertex component analysis: a fast algorithm to unmix hyperspectral data (2005, background): “Owing to physical constraints [ 19 ], abundance fractions are nonnegative and satisfy the so-called positivity constraint , where is a vector of ones.”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, background): “This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples.”
Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network (2017, background): “Sub-pixel level target detection has also been proposed to detect objects of interest within a pixel [16,17].”
Hyperspectral Image Processing for Automatic Target Detection Applications (2003, methodology): “When P = 1, it can be shown [27] that the amplitude of the target signature in Equation 15 can be estimated by the formula”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “[16] proposed a GLRT detector [72,82,83], relying in the substitution of likelihood ratio by maximum-likelihood estimates of the unknown quantities.”