9 September 2017 Spatial–spectral Schroedinger embedding for target detection in hyperspectral imagery
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Abstract
The Schroedinger eigenmaps (SE) algorithm using spatial and spectral information has been applied to supervised classification of hyperspectral imagery (HSI). We have previously introduced the use of SE in spectral target detection problems. The original SE-based target detector was built on the spectral information encoded in the Laplacian and Schroedinger operators. The original SE-based detector is extended such that spatial connectivity of target-like pixels is explored and encoded into the Schroedinger operator using a “knowledge propagation” scheme. The modified SE-based detector is applied to two HSI data sets that share similar target materials. Receiver operating characteristic curves and rates of detection and false alarm at object level are used as quantitative metrics to assess the detector. In addition, the Schroedinger embedding performance in target detection is compared against the performances of principal component embedding and the Laplacian embedding. Results show that the SE-based detector with spatial–spectral features outperforms the other considered approaches.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Leidy P. Dorado-Munoz, Leidy P. Dorado-Munoz, David W. Messinger, David W. Messinger, } "Spatial–spectral Schroedinger embedding for target detection in hyperspectral imagery," Optical Engineering 56(9), 093101 (9 September 2017). https://doi.org/10.1117/1.OE.56.9.093101 . Submission: Received: 29 March 2017; Accepted: 14 August 2017
Received: 29 March 2017; Accepted: 14 August 2017; Published: 9 September 2017
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