Mr. Qiang Ling Profile

Qiang Ling

at National Univ. of Defense Technology

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Author

Publications (3)

Proceedings Article | 4 October 2021 Paper

Hyperspectral moving target detection algorithm based on multi-scale fusion

Peiying Shi, Qiang Ling, Jing Wu, Zaiping Lin

Proceedings Volume 11929, 1192906 (2021) https://doi.org/10.1117/12.2611286

KEYWORDS: Detection and tracking algorithms, Target detection, Hyperspectral target detection, Hyperspectral imaging, Image fusion, Sensors, Reconstruction algorithms, Cameras, Remote sensing

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Proceedings Article | 5 November 2020 Paper

Matrix capsule network for hyperspectral image classification

Zhengyan Wang, Qiang Lin, Zhaoxu Li, ZaiPing Lin, Jing Wu

Proceedings Volume 11567, 1156721 (2020) https://doi.org/10.1117/12.2579680

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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.

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