Prof. Weidong Sheng Profile

Prof. Weidong Sheng

at National Univ of Defense Technology

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Publications (6)

Proceedings Article | 7 October 2019 Paper

Sparse representation with constraints for target detection in hyperspectral imagery

Qiang Ling, Weidong Sheng, Zaiping Lin, Miao Li, Wei An

Proceedings Volume 11155, 111551G (2019) https://doi.org/10.1117/12.2532761

KEYWORDS: Target detection, Hyperspectral target detection, Detection and tracking algorithms, Sensors, Hyperspectral imaging, Contamination, Statistical modeling

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In this paper, we propose a constrained sparse representation (CSR) based algorithm for target detection in hyperspectral imagery. This algorithm is based on the concept that each pixel lies in a low-dimensional sub- space spanned by target and background training samples. Therefore, it can be linearly represented by these samples weighted by a sparse vector. According to the spectral linear mixture model (LMM), the non-negativity constraint and sum-to-one constraint are imposed to the sparse vector. According to the Karush Kuhn Tucker (KKT) conditions, the upper bound constraint on sparsity level is removed. Besides, to alleviate the effects of target contamination in the background dictionary, an upper bound constraint is given to the weights corresponding to the atoms in the background dictionary. Finally, this constrained sparsity model is solved by a fast sequential minimal optimization (SMO) method. Different from other sparsity-based models, both the residuals and weights are used to detect targets in our algorithm, resulting in a better detection performance. The major advantage of the proposed method is the capability to suppress target signals in the background dictionary. The proposed method was compared to several traditional detectors including spectral matched filter (SMF), adaptive subspace detector (ASD), matched subspace detector (MSD), and sparse representation (SR) based detector. The commonly used receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are adopted for performance evaluation. Extensive experiments are conducted on two real hyperspectral data sets. It is demonstrated that our CSR method is robust to different target contamination levels in the background dictionary. From these experiments, it can be seen that our CSR method achieves a much higher target detection probability than other traditional methods at all false alarm rates. Meanwhile, our CSR method achieves the highest AUC value, which is significantly larger than most traditional methods. Moreover, the proposed method also have a relatively low computational cost.

Proceedings Article | 10 February 2017 Paper

The review of the IR radiation characteristic of exhaust plume of the liquid rocket engine

Hanyang Wu, Weidong Sheng, Wei An, Jian Zeng, Yuanyuan Yang

Proceedings Volume 10250, 1025005 (2017) https://doi.org/10.1117/12.2266751

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Proceedings Article | 12 October 2015 Paper

Pixel partition method using Markov random field for measurements of closely spaced objects by optical sensors

Xueying Wang, Jun Li, Weidong Sheng, Wei An, Qinfeng Du

Proceedings Volume 9639, 963925 (2015) https://doi.org/10.1117/12.2194348

KEYWORDS: Sensors, Magnetorheological finishing, Optical sensors, Target detection, Satellites, Optical tracking, Signal to noise ratio, Lithium, Image processing, Image sensors

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SPIE Journal Paper | 1 November 2012

Bayesian approach to joint super-resolution and trajectory estimation for midcourse closely spaced objects via space-based infrared sensor

Liangkui Lin, Weidong Sheng, Dan Xu

OE, Vol. 51, Issue 11, 117003, (November 2012) https://doi.org/10.1117/12.10.1117/1.OE.51.11.117003

KEYWORDS: Super resolution, Signal to noise ratio, Infrared sensors, Sensors, Monte Carlo methods, Optical engineering, Infrared imaging, Error analysis, Data modeling, Bayesian inference

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SPIE Journal Paper | 1 November 2011

Tracking a large number of closely spaced objects based on the particle probability hypothesis density filter via optical sensor

Liangkui Lin, Hui Xu, Wei An, Weidong Sheng, Dan Xu

OE, Vol. 50, Issue 11, 116401, (November 2011) https://doi.org/10.1117/12.10.1117/1.3651798

KEYWORDS: Particles, Particle filters, Electronic filtering, Optical tracking, Staring arrays, Image filtering, Optical engineering, Filtering (signal processing), Optical filters, Video

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Showing 5 of 6 publications

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